From 7cedffdd1ebc7ee1dfaf5f5f622a8dba22176623 Mon Sep 17 00:00:00 2001 From: DidierMurilloF <79462830+DidierMurilloF@users.noreply.github.com> Date: Fri, 26 Jul 2024 20:43:41 +0000 Subject: [PATCH] =?UTF-8?q?Deploying=20to=20gh-pages=20from=20@=20DidierMu?= =?UTF-8?q?rilloF/FielDHub@a6114452c2df976c114ba30613938996cbb963e3=20?= =?UTF-8?q?=F0=9F=9A=80?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- articles/row_column.html | 42 ++++++------- pkgdown.yml | 2 +- reference/row_column.html | 120 ++++++++++++++++++-------------------- search.json | 2 +- 4 files changed, 80 insertions(+), 86 deletions(-) diff --git a/articles/row_column.html b/articles/row_column.html index 564030a..a51d95e 100644 --- a/articles/row_column.html +++ b/articles/row_column.html @@ -98,7 +98,7 @@

Row-Column Design

function row_column() from the FielDHub package.

-

Resolvable Row-Column Design (Two Stage Generation) +

Resolvable Row-Column Design (Two-Step Optimization)

It randomly generates a resolvable row-column design.The design is optimized in both rows and columns blocking factors. The randomization @@ -129,7 +129,7 @@

1. Using the FielDHub Shiny Appprint().

 print(rcd)
-
Resolvable Row-Column Design (Two Stage Generation) 
+
Resolvable Row-Column Design (Two-Step Optimization) 
 
 Efficiency of design: 
    Level Blocks D-Efficiency A-Efficiency   A-Bound
@@ -351,15 +351,15 @@ 

+6   2    FARGO  102   1   1      2    22      G-22
+11  3    FARGO  103   1   1      3    28      G-28
+16  4    FARGO  104   1   1      4     1       G-1
+21  5    FARGO  105   1   1      5    13      G-13
+26  6    FARGO  106   1   1      6    15      G-15
+31  7    FARGO  107   1   1      7    37      G-37
+36  8    FARGO  108   1   1      8    42      G-42
+41  9    FARGO  109   1   1      9    39      G-39
+2  10    FARGO  110   1   2      1    11      G-11

Access to rcd object @@ -380,15 +380,15 @@

Access to rcd objecthead(rcd$fieldBook, 10)

   ID LOCATION PLOT REP ROW COLUMN ENTRY TREATMENT
 1   1    FARGO  101   1   1      1    23      G-23
-6   6    FARGO  102   1   1      2    22      G-22
-11 11    FARGO  103   1   1      3    28      G-28
-16 16    FARGO  104   1   1      4     1       G-1
-21 21    FARGO  105   1   1      5    13      G-13
-26 26    FARGO  106   1   1      6    15      G-15
-31 31    FARGO  107   1   1      7    37      G-37
-36 36    FARGO  108   1   1      8    42      G-42
-41 41    FARGO  109   1   1      9    39      G-39
-2   2    FARGO  110   1   2      1    11      G-11
+6 2 FARGO 102 1 1 2 22 G-22 +11 3 FARGO 103 1 1 3 28 G-28 +16 4 FARGO 104 1 1 4 1 G-1 +21 5 FARGO 105 1 1 5 13 G-13 +26 6 FARGO 106 1 1 6 15 G-15 +31 7 FARGO 107 1 1 7 37 G-37 +36 8 FARGO 108 1 1 8 42 G-42 +41 9 FARGO 109 1 1 9 39 G-39 +2 10 FARGO 110 1 2 1 11 G-11

Plot the field layout diff --git a/pkgdown.yml b/pkgdown.yml index cc274b7..6b4960a 100644 --- a/pkgdown.yml +++ b/pkgdown.yml @@ -24,7 +24,7 @@ articles: split_split_plot: split_split_plot.html square_lattice: square_lattice.html strip_plot: strip_plot.html -last_built: 2024-07-23T15:03Z +last_built: 2024-07-26T20:41Z urls: reference: https://didiermurillof.github.io/FielDHub/reference article: https://didiermurillof.github.io/FielDHub/articles diff --git a/reference/row_column.html b/reference/row_column.html index 2fa01e8..4171a1a 100644 --- a/reference/row_column.html +++ b/reference/row_column.html @@ -173,13 +173,19 @@

Author<

Examples


-# Example 1: Generates a row-column design with 3 full blocks and 24 treatments
-# and 6 rows. This for one location.
-rowcold1 <- row_column(t = 24, nrows = 6, r = 3, l = 1, 
-                       plotNumber= 101, 
-                       locationNames = "Loc1",
-                       iterations = 500,
-                       seed = 21)
+# Example 1: Generates a row-column design with 2 full blocks and 24 treatments
+# and 6 rows. This for one location. This example uses 100 iterations for the optimization
+# but 1000 is the default and recomended value.
+rowcold1 <- row_column(
+  t = 24, 
+  nrows = 6, 
+  r = 2, 
+  l = 1, 
+  plotNumber= 101, 
+  locationNames = "Loc1",
+  iterations = 100,
+  seed = 21
+)
 rowcold1$infoDesign
 #> $rows
 #> [1] 6
@@ -188,7 +194,7 @@ 
Examples#> [1] 4
 #> 
 #> $reps
-#> [1] 3
+#> [1] 2
 #> 
 #> $treatments
 #> [1] 24
@@ -225,33 +231,25 @@ 
Examples#> [5,]   NA   NA   NA   NA
 #> [6,]   NA   NA   NA   NA
 #> 
-#> $Loc_Loc1$rep3
-#>      [,1] [,2] [,3] [,4]
-#> [1,]   NA   NA   NA   NA
-#> [2,]   NA   NA   NA   NA
-#> [3,]   NA   NA   NA   NA
-#> [4,]   NA   NA   NA   NA
-#> [5,]   NA   NA   NA   NA
-#> [6,]   NA   NA   NA   NA
-#> 
 #> 
 head(rowcold1$fieldBook,12)
 #>    ID LOCATION PLOT REP ROW COLUMN ENTRY TREATMENT
-#> 1   1     Loc1  101   1   1      1    24      G-24
-#> 7   7     Loc1  102   1   1      2    22      G-22
-#> 13 13     Loc1  103   1   1      3    16      G-16
-#> 19 19     Loc1  104   1   1      4     4       G-4
-#> 2   2     Loc1  105   1   2      1    15      G-15
-#> 8   8     Loc1  106   1   2      2     1       G-1
-#> 14 14     Loc1  107   1   2      3     8       G-8
-#> 20 20     Loc1  108   1   2      4     3       G-3
-#> 3   3     Loc1  109   1   3      1    21      G-21
-#> 9   9     Loc1  110   1   3      2    11      G-11
-#> 15 15     Loc1  111   1   3      3    10      G-10
-#> 21 21     Loc1  112   1   3      4     7       G-7
+#> 1   1     Loc1  101   1   1      1    13      G-13
+#> 7   2     Loc1  102   1   1      2    23      G-23
+#> 13  3     Loc1  103   1   1      3    10      G-10
+#> 19  4     Loc1  104   1   1      4    12      G-12
+#> 2   5     Loc1  105   1   2      1    20      G-20
+#> 8   6     Loc1  106   1   2      2     8       G-8
+#> 14  7     Loc1  107   1   2      3     6       G-6
+#> 20  8     Loc1  108   1   2      4    19      G-19
+#> 3   9     Loc1  109   1   3      1    24      G-24
+#> 9  10     Loc1  110   1   3      2    11      G-11
+#> 15 11     Loc1  111   1   3      3    21      G-21
+#> 21 12     Loc1  112   1   3      4     5       G-5
 
-# Example 2: Generates a row-column design with 3 full blocks and 30 treatments
-# and 5 rows, for one location.
+# Example 2: Generates a row-column design with 2 full blocks and 30 treatments
+# and 5 rows, for one location. This example uses 100 iterations for the optimization
+# but 1000 is the default and recommended value.
 # In this case, we show how to use the option data.
 treatments <- paste("ND-", 1:30, sep = "")
 ENTRY <- 1:30
@@ -264,13 +262,17 @@ 
Examples#> 4     4      ND-4
 #> 5     5      ND-5
 #> 6     6      ND-6
-rowcold2 <- row_column(t = 30, nrows = 5, r = 3, l = 1, 
-                       plotNumber= c(101,1001), 
-                       locationNames = c("A", "B"),
-                       seed = 15,
-                       iterations = 500,
-                       data = treatment_list)
-#> Warning: Length of plot numbers is larger than number of locations.
+rowcold2 <- row_column(
+  t = 30, 
+  nrows = 5, 
+  r = 2, 
+  l = 1, 
+  plotNumber= 1001, 
+  locationNames = "A",
+  seed = 15,
+  iterations = 100,
+  data = treatment_list
+)
 rowcold2$infoDesign
 #> $rows
 #> [1] 5
@@ -279,7 +281,7 @@ 
Examples#> [1] 6
 #> 
 #> $reps
-#> [1] 3
+#> [1] 2
 #> 
 #> $treatments
 #> [1] 30
@@ -288,7 +290,7 @@ 
Examples#> [1] 1
 #> 
 #> $location_names
-#> [1] 1
+#> [1] "A"
 #> 
 #> $seed
 #> [1] 15
@@ -297,16 +299,8 @@ 
Examples#> [1] 9
 #> 
 rowcold2$resolvableBlocks
-#> $Loc_1
-#> $Loc_1$rep1
-#>      [,1] [,2] [,3] [,4] [,5] [,6]
-#> [1,] NA   NA   NA   NA   NA   NA  
-#> [2,] NA   NA   NA   NA   NA   NA  
-#> [3,] NA   NA   NA   NA   NA   NA  
-#> [4,] NA   NA   NA   NA   NA   NA  
-#> [5,] NA   NA   NA   NA   NA   NA  
-#> 
-#> $Loc_1$rep2
+#> $Loc_A
+#> $Loc_A$rep1
 #>      [,1] [,2] [,3] [,4] [,5] [,6]
 #> [1,] NA   NA   NA   NA   NA   NA  
 #> [2,] NA   NA   NA   NA   NA   NA  
@@ -314,7 +308,7 @@ 
Examples#> [4,] NA   NA   NA   NA   NA   NA  
 #> [5,] NA   NA   NA   NA   NA   NA  
 #> 
-#> $Loc_1$rep3
+#> $Loc_A$rep2
 #>      [,1] [,2] [,3] [,4] [,5] [,6]
 #> [1,] NA   NA   NA   NA   NA   NA  
 #> [2,] NA   NA   NA   NA   NA   NA  
@@ -325,18 +319,18 @@ 
Examples#> 
 head(rowcold2$fieldBook,12)
 #>    ID LOCATION PLOT REP ROW COLUMN ENTRY TREATMENT
-#> 1   1        1  101   1   1      1    26     ND-26
-#> 6   6        1  102   1   1      2     8      ND-8
-#> 11 11        1  103   1   1      3     9      ND-9
-#> 16 16        1  104   1   1      4    18     ND-18
-#> 21 21        1  105   1   1      5    19     ND-19
-#> 26 26        1  106   1   1      6    21     ND-21
-#> 2   2        1  107   1   2      1    11     ND-11
-#> 7   7        1  108   1   2      2    15     ND-15
-#> 12 12        1  109   1   2      3    22     ND-22
-#> 17 17        1  110   1   2      4    24     ND-24
-#> 22 22        1  111   1   2      5     7      ND-7
-#> 27 27        1  112   1   2      6    28     ND-28
+#> 1   1        A 1001   1   1      1     5      ND-5
+#> 6   2        A 1002   1   1      2     7      ND-7
+#> 11  3        A 1003   1   1      3    14     ND-14
+#> 16  4        A 1004   1   1      4    23     ND-23
+#> 21  5        A 1005   1   1      5     9      ND-9
+#> 26  6        A 1006   1   1      6    15     ND-15
+#> 2   7        A 1007   1   2      1    10     ND-10
+#> 7   8        A 1008   1   2      2    17     ND-17
+#> 12  9        A 1009   1   2      3    13     ND-13
+#> 17 10        A 1010   1   2      4     6      ND-6
+#> 22 11        A 1011   1   2      5    29     ND-29
+#> 27 12        A 1012   1   2      6    20     ND-20
diff --git a/search.json b/search.json index b30ca68..f26707d 100644 --- a/search.json +++ b/search.json @@ -1 +1 @@ -[{"path":[]},{"path":"https://didiermurillof.github.io/FielDHub/CODE_OF_CONDUCT.html","id":"our-pledge","dir":"","previous_headings":"","what":"Our Pledge","title":"Contributor Covenant Code of Conduct","text":"members, contributors, leaders pledge make participation community harassment-free experience everyone, regardless age, body size, visible invisible disability, ethnicity, sex characteristics, gender identity expression, level experience, education, socio-economic status, nationality, personal appearance, race, religion, sexual identity orientation. pledge act interact ways contribute open, welcoming, diverse, inclusive, healthy community.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/CODE_OF_CONDUCT.html","id":"our-standards","dir":"","previous_headings":"","what":"Our Standards","title":"Contributor Covenant Code of Conduct","text":"Examples behavior contributes positive environment community include: Demonstrating empathy kindness toward people respectful differing opinions, viewpoints, experiences Giving gracefully accepting constructive feedback Accepting responsibility apologizing affected mistakes, learning experience Focusing best just us individuals, overall community Examples unacceptable behavior include: use sexualized language imagery, sexual attention advances kind Trolling, insulting derogatory comments, personal political attacks Public private harassment Publishing others’ private information, physical email address, without explicit permission conduct reasonably considered inappropriate professional setting","code":""},{"path":"https://didiermurillof.github.io/FielDHub/CODE_OF_CONDUCT.html","id":"enforcement-responsibilities","dir":"","previous_headings":"","what":"Enforcement Responsibilities","title":"Contributor Covenant Code of Conduct","text":"Community leaders responsible clarifying enforcing standards acceptable behavior take appropriate fair corrective action response behavior deem inappropriate, threatening, offensive, harmful. Community leaders right responsibility remove, edit, reject comments, commits, code, wiki edits, issues, contributions aligned Code Conduct, communicate reasons moderation decisions appropriate.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/CODE_OF_CONDUCT.html","id":"scope","dir":"","previous_headings":"","what":"Scope","title":"Contributor Covenant Code of Conduct","text":"Code Conduct applies within community spaces, also applies individual officially representing community public spaces. Examples representing community include using official e-mail address, posting via official social media account, acting appointed representative online offline event.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/CODE_OF_CONDUCT.html","id":"enforcement","dir":"","previous_headings":"","what":"Enforcement","title":"Contributor Covenant Code of Conduct","text":"Instances abusive, harassing, otherwise unacceptable behavior may reported community leaders responsible enforcement [email][didier.murilloflorez@gmail.com]. complaints reviewed investigated promptly fairly. community leaders obligated respect privacy security reporter incident.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/CODE_OF_CONDUCT.html","id":"enforcement-guidelines","dir":"","previous_headings":"","what":"Enforcement Guidelines","title":"Contributor Covenant Code of Conduct","text":"Community leaders follow Community Impact Guidelines determining consequences action deem violation Code Conduct:","code":""},{"path":"https://didiermurillof.github.io/FielDHub/CODE_OF_CONDUCT.html","id":"id_1-correction","dir":"","previous_headings":"Enforcement Guidelines","what":"1. Correction","title":"Contributor Covenant Code of Conduct","text":"Community Impact: Use inappropriate language behavior deemed unprofessional unwelcome community. Consequence: private, written warning community leaders, providing clarity around nature violation explanation behavior inappropriate. public apology may requested.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/CODE_OF_CONDUCT.html","id":"id_2-warning","dir":"","previous_headings":"Enforcement Guidelines","what":"2. Warning","title":"Contributor Covenant Code of Conduct","text":"Community Impact: violation single incident series actions. Consequence: warning consequences continued behavior. interaction people involved, including unsolicited interaction enforcing Code Conduct, specified period time. includes avoiding interactions community spaces well external channels like social media. 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Permanent Ban","title":"Contributor Covenant Code of Conduct","text":"Community Impact: Demonstrating pattern violation community standards, including sustained inappropriate behavior, harassment individual, aggression toward disparagement classes individuals. Consequence: permanent ban sort public interaction within community.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/CODE_OF_CONDUCT.html","id":"attribution","dir":"","previous_headings":"","what":"Attribution","title":"Contributor Covenant Code of Conduct","text":"Code Conduct adapted Contributor Covenant, version 2.0, available https://www.contributor-covenant.org/version/2/0/code_of_conduct.html. Community Impact Guidelines inspired Mozilla’s code conduct enforcement ladder. answers common questions code conduct, see FAQ https://www.contributor-covenant.org/faq. 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Let others discover , telling person, via Twitter blog post. Using FielDHub paper writing? Consider citing .","code":""},{"path":"https://didiermurillof.github.io/FielDHub/CONTRIBUTING.html","id":"ask-a-question-️","dir":"","previous_headings":"How you can contribute","what":"Ask a question ⁉️","title":"Contributing to FielDHub","text":"Using FielDHub got stuck? Browse documentation see can find solution. Still stuck? Post question issue GitHub. offer user support, ’ll try best address , questions often lead better documentation discovery bugs. Want ask question private? Contact package maintainer [email][didier.murilloflorez@ndsu.edu].","code":""},{"path":"https://didiermurillof.github.io/FielDHub/CONTRIBUTING.html","id":"propose-an-idea-","dir":"","previous_headings":"How you can contribute","what":"Propose an idea πŸ’‘","title":"Contributing to FielDHub","text":"idea new FielDHub feature? Take look documentation issue list see isn’t included suggested yet. , suggest idea issue GitHub. can’t promise implement idea, helps : Explain detail work. Keep scope narrow possible. See want contribute code idea well.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/CONTRIBUTING.html","id":"report-a-bug-","dir":"","previous_headings":"How you can contribute","what":"Report a bug πŸ›","title":"Contributing to FielDHub","text":"Using FielDHub discovered bug? ’s annoying! Don’t let others experience report issue GitHub can fix . good bug report makes easier us , please include: operating system name version (e.g.Β Mac OS 10.13.6). details local setup might helpful troubleshooting. Detailed steps reproduce bug.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/CONTRIBUTING.html","id":"improve-the-documentation-","dir":"","previous_headings":"How you can contribute","what":"Improve the documentation πŸ“–","title":"Contributing to FielDHub","text":"Noticed typo website? Think function use better example? Good documentation makes difference, help improve welcome!","code":""},{"path":"https://didiermurillof.github.io/FielDHub/CONTRIBUTING.html","id":"the-website","dir":"","previous_headings":"How you can contribute > Improve the documentation πŸ“–","what":"The website","title":"Contributing to FielDHub","text":"website generated pkgdown. means don’t write html: content pulled together documentation code, vignettes, Markdown files, package DESCRIPTION _pkgdown.yml settings. know way around pkgdown, can propose file change improve documentation. , report issue can point right direction.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/CONTRIBUTING.html","id":"function-documentation","dir":"","previous_headings":"How you can contribute > Improve the documentation πŸ“–","what":"Function documentation","title":"Contributing to FielDHub","text":"Functions described comments near code translated documentation using roxygen2. want improve function description: Go R/ directory code repository. Look file name function. Propose file change update function documentation roxygen comments (starting #').","code":""},{"path":"https://didiermurillof.github.io/FielDHub/CONTRIBUTING.html","id":"contribute-code-","dir":"","previous_headings":"How you can contribute","what":"Contribute code πŸ“","title":"Contributing to FielDHub","text":"Care fix bugs implement new functionality FielDHub? Awesome! πŸ‘ look issue list leave comment things want work . See also development guidelines .","code":""},{"path":"https://didiermurillof.github.io/FielDHub/CONTRIBUTING.html","id":"development-guidelines","dir":"","previous_headings":"","what":"Development guidelines","title":"Contributing to FielDHub","text":"try follow GitHub flow development. Fork repo clone computer. learn process, see guide. forked cloned project since worked , pull changes original repo clone using git pull upstream master. Open RStudio project file (.Rproj). Write code. Test code (bonus points adding unit tests). Document code (see function documentation ). Check code devtools::check() aim 0 errors, warnings notes. Commit push changes. 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SOFTWARE PROVIDED β€œβ€, WITHOUT WARRANTY KIND, EXPRESS IMPLIED, INCLUDING LIMITED WARRANTIES MERCHANTABILITY, FITNESS PARTICULAR PURPOSE NONINFRINGEMENT. EVENT SHALL AUTHORS COPYRIGHT HOLDERS LIABLE CLAIM, DAMAGES LIABILITY, WHETHER ACTION CONTRACT, TORT OTHERWISE, ARISING , CONNECTION SOFTWARE USE DEALINGS SOFTWARE.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/RCBD_augmented.html","id":"overview","dir":"Articles","previous_headings":"","what":"Overview","title":"Augmented Randomized Complete Block Design","text":"augmented randomized complete block design another option overcome problem limited facilities lack seed researchers want test many treatments. kind design, approach build augmented blocks allocate amount controls every block along treatments. FielDHub includes function run experimental designs, features include options set number entries number checks augmented blocks experiment. Users can also choose run experiment multiple locations.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/RCBD_augmented.html","id":"use-case","dir":"Articles","previous_headings":"","what":"Use case","title":"Augmented Randomized Complete Block Design","text":"example, say project needs test 120 genotypes cassava two locations. addition, research includes four checks six augmented blocks carry experiment. design setup comes 6 blocks size 24 plots total 144 plots distributed field 12 rows 12 columns.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/RCBD_augmented.html","id":"running-the-shiny-app","dir":"Articles","previous_headings":"","what":"Running the Shiny App","title":"Augmented Randomized Complete Block Design","text":"launch app need run either ","code":"FielDHub::run_app() library(FielDHub) run_app()"},{"path":"https://didiermurillof.github.io/FielDHub/articles/RCBD_augmented.html","id":"using-the-fieldhub-shiny-app","dir":"Articles","previous_headings":"","what":"1. Using the FielDHub Shiny App","title":"Augmented Randomized Complete Block Design","text":"app running, go Unreplicated Designs > RCBD Augmented , follow following steps show generate RCBD Augmented.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/RCBD_augmented.html","id":"inputs","dir":"Articles","previous_headings":"","what":"Inputs","title":"Augmented Randomized Complete Block Design","text":"selection , means app going generate synthetic data entries names treatment/genotypes based user inputs. selection Yes, entries list must fulfill specific format must .csv file. file must columns ENTRY NAME. ENTRY column must unique entry integer number treatment/genotype. column NAME must unique name identifies treatment/genotype. ENTRY NAME must unique, duplicates allowed. following table, show example entries list format. example entry list four checks 8 treatments/genotypes. crucial allocate checks top part file. Enter number stacked experiments Input # Stacked Expts box. means number times experiment replicated. case perform just 1 experiment. augmented RCBD option choose whether randomize entries , Randomize Entries toggle button. recommended always randomized treatments/entries researchers choose randomize treatments, often due logistical issues. Enter number entries/treatments Input # Entries box, 120 example experiment. Set number checks per block Checks per Block box. case, 5 checks. Set number blocks Input # Blocks box, 6 example. total number plots field Input # Stacked Expts(Input # Entries + Input # Blocks * Checks per Block), per location. Enter number locations Input # Locations. Set 2. Select serpentine cartesian Plot Order Layout. example set serpentine layout. ensure randomizations consistent across sessions, can set random seed box labeled random seed. example, set 1987. Enter starting plot number Starting Plot Number box. experiment multiple locations, must enter comma separated list numbers length number locations input valid. entered information experiment left side panel, click Run! button run design. prompted select dimensions field list options drop middle screen box labeled Select dimensions field. case, select 12 x 12. Click Randomize! button randomize experiment set field dimensions see output plots. change dimensions , must re-randomize. change inputs left side panel running experiment initially, click Run Randomize buttons , re-run new inputs.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/RCBD_augmented.html","id":"outputs","dir":"Articles","previous_headings":"","what":"Outputs","title":"Augmented Randomized Complete Block Design","text":"run augmented RCBD FielDHub set dimensions field, several ways display information contained field book. first tab, Get Random, shows option change dimensions field re-randomize, well reference guide experiment design.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/RCBD_augmented.html","id":"input-data","dir":"Articles","previous_headings":"Outputs","what":"Input Data","title":"Augmented Randomized Complete Block Design","text":"second tab, Input Data, can see entries randomization list, well table checks number times appear field. list entries, reps check included well.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/RCBD_augmented.html","id":"randomized-field","dir":"Articles","previous_headings":"Outputs","what":"Randomized Field","title":"Augmented Randomized Complete Block Design","text":"Randomized Field tab displays graphical representation randomization entries field specified dimensions. checks colored uniquely, showing number times distributed throughout field. display includes numbered labels rows columns. can copy field table save directly Excel file Copy Excel buttons top.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/RCBD_augmented.html","id":"plot-number-field","dir":"Articles","previous_headings":"Outputs","what":"Plot Number Field","title":"Augmented Randomized Complete Block Design","text":"Plot Number Field tab, table display field plots numbered according Plot Order Layout specified, either serpentine cartesian. can see corresponding entries plot number field book. Like Randomized Field tab, can copy table save Excel file Copy Excel buttons.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/RCBD_augmented.html","id":"field-book","dir":"Articles","previous_headings":"Outputs","what":"Field Book","title":"Augmented Randomized Complete Block Design","text":"Field Book displays information experimental design table format. contains specific plot number row column address entry, well corresponding treatment plot. table searchable, can filter data relevant columns.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/RCBD_augmented.html","id":"using-the-fieldhub-function-rcbd_augmented","dir":"Articles","previous_headings":"","what":"2. Using the FielDHub function: RCBD_augmented()","title":"Augmented Randomized Complete Block Design","text":"can run design function RCBD_augmented() FielDHub package. First, need load FielDHub package typing, , can enter information describing design like :","code":"library(FielDHub) aug_RCBD <- RCBD_augmented( lines = 120, checks = 4, b = 6, repsExpt = 1, l = 2, random = TRUE, exptName = \"Cassava_2022\", plotNumber = c(1001, 2001), locationNames = c(\"FARGO\", \"CASSELTON\"), nrows = 12, ncols = 12, seed = 1987 )"},{"path":"https://didiermurillof.github.io/FielDHub/articles/RCBD_augmented.html","id":"details-on-the-inputs-entered-in-rcbd_augmented-above","dir":"Articles","previous_headings":"2. Using the FielDHub function: RCBD_augmented()","what":"Details on the inputs entered in RCBD_augmented() above","title":"Augmented Randomized Complete Block Design","text":"description inputs used generate design, lines = 120 number entries checks = 4 number checks augmented block. b = 6 number augmented blocks. repsExpt = 1 number reps experiment. l = 2 number locations. random = TRUE means treatment/entries checks randomized. exptName = \"Cassava_2022\" optional name experiment. plotNumber = c(1001,2001) starting plot number location respectively, single number 1 location. locationNames = c(\"FARGO\", \"CASSELTON\") values representing respective name location. nrows = 12 number rows field. optional ncols = 12 number columns field. optional. seed = 1987 random seed replicate identical randomizations.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/RCBD_augmented.html","id":"print-aug_rcbd-output","dir":"Articles","previous_headings":"2. Using the FielDHub function: RCBD_augmented()","what":"Print aug_RCBD output","title":"Augmented Randomized Complete Block Design","text":"print summary information object aug_RCBD, can use generic function print().","code":"print(aug_RCBD) Augmented Randomized Complete Block Design: Information on the design parameters: List of 11 $ rows : num 12 $ columns : num 12 $ rows_within_blocks : num 2 $ columns_within_blocks: num 12 $ treatments : num 120 $ checks : num 4 $ blocks : num 6 $ plots_per_block : num [1:6] 24 24 24 24 24 24 $ locations : num 2 $ fillers : num 0 $ seed : num 1987 10 First observations of the data frame with the RCBD_augmented field book: ID EXPT LOCATION YEAR PLOT ROW COLUMN CHECKS BLOCK ENTRY TREATMENT 1 1 Cassava_2022 FARGO 2024 1001 1 1 0 1 98 G98 2 2 Cassava_2022 FARGO 2024 1002 1 2 0 1 103 G103 3 3 Cassava_2022 FARGO 2024 1003 1 3 0 1 87 G87 4 4 Cassava_2022 FARGO 2024 1004 1 4 1 1 2 CH2 5 5 Cassava_2022 FARGO 2024 1005 1 5 0 1 21 G21 6 6 Cassava_2022 FARGO 2024 1006 1 6 0 1 122 G122 7 7 Cassava_2022 FARGO 2024 1007 1 7 1 1 4 CH4 8 8 Cassava_2022 FARGO 2024 1008 1 8 0 1 44 G44 9 9 Cassava_2022 FARGO 2024 1009 1 9 0 1 23 G23 10 10 Cassava_2022 FARGO 2024 1010 1 10 0 1 113 G113"},{"path":"https://didiermurillof.github.io/FielDHub/articles/RCBD_augmented.html","id":"access-to-aug_rcbd-output","dir":"Articles","previous_headings":"2. Using the FielDHub function: RCBD_augmented()","what":"Access to aug_RCBD output","title":"Augmented Randomized Complete Block Design","text":"function RCBD_augmented() returns list consisting information displayed output tabs FielDHub app: design information, plot layout, plot numbering, entries list, field book. accessible $ operator, .e.Β aug_RCBD$layoutRandom aug_RCBD$fieldBook. aug_RCBD$fieldBook list containing information every plot field, information location plot treatment plot. seen output , field book columns ID, EXPT, LOCATION, YEAR, PLOT, ROW, COLUMN, CHECKS, ENTRY, TREATMENT. Let us see first 10 rows field book experiment.","code":"field_book <- aug_RCBD$fieldBook head(field_book, 10) ID EXPT LOCATION YEAR PLOT ROW COLUMN CHECKS BLOCK ENTRY TREATMENT 1 1 Cassava_2022 FARGO 2024 1001 1 1 0 1 98 G98 2 2 Cassava_2022 FARGO 2024 1002 1 2 0 1 103 G103 3 3 Cassava_2022 FARGO 2024 1003 1 3 0 1 87 G87 4 4 Cassava_2022 FARGO 2024 1004 1 4 1 1 2 CH2 5 5 Cassava_2022 FARGO 2024 1005 1 5 0 1 21 G21 6 6 Cassava_2022 FARGO 2024 1006 1 6 0 1 122 G122 7 7 Cassava_2022 FARGO 2024 1007 1 7 1 1 4 CH4 8 8 Cassava_2022 FARGO 2024 1008 1 8 0 1 44 G44 9 9 Cassava_2022 FARGO 2024 1009 1 9 0 1 23 G23 10 10 Cassava_2022 FARGO 2024 1010 1 10 0 1 113 G113"},{"path":"https://didiermurillof.github.io/FielDHub/articles/RCBD_augmented.html","id":"plot-field-layout","dir":"Articles","previous_headings":"2. Using the FielDHub function: RCBD_augmented()","what":"Plot field layout","title":"Augmented Randomized Complete Block Design","text":"plotting layout function coordinates ROW COLUMN field book object can use generic function plot() follow,","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/RCBD_augmented.html","id":"plot-layout-for-location-1","dir":"Articles","previous_headings":"2. Using the FielDHub function: RCBD_augmented() > Plot field layout","what":"Plot layout for location 1","title":"Augmented Randomized Complete Block Design","text":"possible pass arguments plot() specific location. example, can plot layout location 2.","code":"plot(aug_RCBD)"},{"path":"https://didiermurillof.github.io/FielDHub/articles/RCBD_augmented.html","id":"plot-layout-for-location-2","dir":"Articles","previous_headings":"2. Using the FielDHub function: RCBD_augmented() > Plot field layout","what":"Plot layout for location 2","title":"Augmented Randomized Complete Block Design","text":"","code":"plot(aug_RCBD, l = 2)"},{"path":"https://didiermurillof.github.io/FielDHub/articles/alpha_lattice.html","id":"using-the-fieldhub-shiny-app","dir":"Articles","previous_headings":"","what":"1. Using the FielDHub Shiny App","title":"Alpha Lattice Design","text":"launch app need run either app running, go Lattice Designs > Alpha Lattice , follow following steps show generate kind design 55 treatments 3 reps.","code":"FielDHub::run_app() library(FielDHub) run_app()"},{"path":"https://didiermurillof.github.io/FielDHub/articles/alpha_lattice.html","id":"inputs","dir":"Articles","previous_headings":"","what":"Inputs","title":"Alpha Lattice Design","text":"selection , means app going generate synthetic data entries names treatment/genotypes based user inputs. selection Yes, entries list must fulfill specific format must .csv file. file must columns ENTRY NAME. ENTRY column must unique entry integer number treatment. column NAME must unique name identifies treatment. ENTRY NAME must unique, duplicates allowed. following table, show example entries list format. example entry list 12 treatments. Input number treatments Input # Treatments box. alpha lattice design, number treatments must composite number. case, set 55. Select number replications treatments Input # Full Reps box. Set 3. Set number plots incomplete block Input # Plots per IBlock box. Set 5. Enter number locations Input # Locations. run experiment single location, set 1. Select serpentine cartesian Plot Order Layout. example use default cartesian layout. Enter starting plot number Starting Plot Number box. experiment multiple locations, must enter comma separated list numbers length number locations input valid. Set 101. Enter name location experiment Input Location box. multiple locations, name must comma separated list. example, set \"FARGO\". ensure randomizations consistent across sessions, can set random seed box labeled random seed. example, set 1235. entered information experiment left side panel, click Run! button run design.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/alpha_lattice.html","id":"outputs","dir":"Articles","previous_headings":"","what":"Outputs","title":"Alpha Lattice Design","text":"run alpha lattice design FielDHub, several ways display information contained field book.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/alpha_lattice.html","id":"field-layout","dir":"Articles","previous_headings":"Outputs","what":"Field Layout","title":"Alpha Lattice Design","text":"first click run button alpha lattice design, FielDHub displays Field Layout tab, shows entries arrangement field. box display, can change layout field change location displayed. can also display heatmap field changing Type Plot Heatmap. view heatmap, must first simulate experiment described field Simulate! button. pop-window appear can enter variable want simulate along minimum maximum values.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/alpha_lattice.html","id":"field-book","dir":"Articles","previous_headings":"Outputs","what":"Field Book","title":"Alpha Lattice Design","text":"Field Book displays information experimental design table format. contains specific plot number row column address entry, well corresponding treatment plot. table searchable, can filter data relevant columns. simulated data heatmap, additional column variable appears Field Book.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/alpha_lattice.html","id":"using-the-fieldhub-function-alpha_lattice","dir":"Articles","previous_headings":"","what":"2. Using the FielDHub function: alpha_lattice()","title":"Alpha Lattice Design","text":"can run design function FielDHub package, alpha_lattice(). First, need load FielDHub package typing, , can enter information describing design like :","code":"library(FielDHub) alpha <- alpha_lattice( t = 55, r = 3, k = 5, l = 1, plotNumber = 101, locationNames = \"FARGO\", seed = 1235 )"},{"path":"https://didiermurillof.github.io/FielDHub/articles/alpha_lattice.html","id":"details-on-the-inputs-entered-in-alpha_lattice-above","dir":"Articles","previous_headings":"2. Using the FielDHub function: alpha_lattice()","what":"Details on the inputs entered in alpha_lattice() above","title":"Alpha Lattice Design","text":"description inputs used generate design, t = 55 number treatments. r=3 number replicates. k = 5 number plots per incomplete block. l = 1 number locations. plotNumber = 101 starting plot number. locationNames = \"FARGO\" optional name location. seed = 1235 random seed replicate identical randomizations.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/alpha_lattice.html","id":"print-alpha-object","dir":"Articles","previous_headings":"2. Using the FielDHub function: alpha_lattice()","what":"Print alpha object","title":"Alpha Lattice Design","text":"print summary information object alpha, can use generic function print().","code":"print(alpha) Alpha Lattice Design Efficiency of design: Level Blocks D-Efficiency A-Efficiency A-Bound 1 1 3 1.0000000 1.0000000 1.0000000 2 2 33 0.7857467 0.7545589 0.7574115 Information on the design parameters: List of 7 $ Reps : num 3 $ iBlocks : num 11 $ NumberTreatments: num 55 $ NumberLocations : num 1 $ Locations : chr \"FARGO\" $ seed : num 1235 $ lambda : num 0.222 10 First observations of the data frame with the alpha_lattice field book: ID LOCATION PLOT REP IBLOCK UNIT ENTRY TREATMENT 1 1 FARGO 101 1 1 1 15 G-15 2 2 FARGO 102 1 1 2 8 G-8 3 3 FARGO 103 1 1 3 51 G-51 4 4 FARGO 104 1 1 4 54 G-54 5 5 FARGO 105 1 1 5 4 G-4 6 6 FARGO 106 1 2 1 50 G-50 7 7 FARGO 107 1 2 2 40 G-40 8 8 FARGO 108 1 2 3 42 G-42 9 9 FARGO 109 1 2 4 22 G-22 10 10 FARGO 110 1 2 5 16 G-16"},{"path":"https://didiermurillof.github.io/FielDHub/articles/alpha_lattice.html","id":"access-to-alpha-object","dir":"Articles","previous_headings":"2. Using the FielDHub function: alpha_lattice()","what":"Access to alpha object","title":"Alpha Lattice Design","text":"function alpha_lattice() returns list consisting information displayed output tabs FielDHub app: design information, plot layout, plot numbering, entries list, field book. accessible $ operator, .e.Β alpha$layoutRandom alpha$fieldBook. alpha$fieldBook list containing information every plot field, information location plot treatment plot. seen output , field book columns ID, LOCATION, PLOT, REP, IBLOCK, UNIT, ENTRY, TREATMENT.","code":"field_book <- alpha$fieldBook head(alpha$fieldBook, 10) ID LOCATION PLOT REP IBLOCK UNIT ENTRY TREATMENT 1 1 FARGO 101 1 1 1 15 G-15 2 2 FARGO 102 1 1 2 8 G-8 3 3 FARGO 103 1 1 3 51 G-51 4 4 FARGO 104 1 1 4 54 G-54 5 5 FARGO 105 1 1 5 4 G-4 6 6 FARGO 106 1 2 1 50 G-50 7 7 FARGO 107 1 2 2 40 G-40 8 8 FARGO 108 1 2 3 42 G-42 9 9 FARGO 109 1 2 4 22 G-22 10 10 FARGO 110 1 2 5 16 G-16"},{"path":"https://didiermurillof.github.io/FielDHub/articles/alpha_lattice.html","id":"plot-the-field-layout","dir":"Articles","previous_headings":"2. Using the FielDHub function: alpha_lattice()","what":"Plot the field layout","title":"Alpha Lattice Design","text":"plotting layout function coordinates ROW COLUMN, can use generic function plot() follows,","code":"plot(alpha)"},{"path":"https://didiermurillof.github.io/FielDHub/articles/crd.html","id":"using-the-fieldhub-shiny-app","dir":"Articles","previous_headings":"","what":"1. Using the FielDHub Shiny App","title":"Completely Randomized Design","text":"launch app need run either app running, go Designs > Completely Randomized Design (CRD) , follow following steps show generate kind design example 15 treatments 6 reps .","code":"FielDHub::run_app() library(FielDHub) run_app()"},{"path":"https://didiermurillof.github.io/FielDHub/articles/crd.html","id":"inputs","dir":"Articles","previous_headings":"","what":"Inputs","title":"Completely Randomized Design","text":"selection , means app going generate synthetic data entries names treatment/genotypes based user inputs. selection Yes, entries list must fulfill specific format must .csv file. file must single column TREATMENT, containing list unique names identify treatment. Duplicate values allowed, entries must unique. following table, show example entries list format. example entry list ten treatments. Input number treatments Input # Treatments box, 15 case. Select number replications treatments Input # Full Reps box. Set 6. Select serpentine cartesian Plot Order Layout. example use default cartesian layout. Enter starting plot number Starting Plot Number box. Set 101. Enter name location experiment Input Location box. completely randomized design can run single location time. Set FARGO. ensure randomizations consistent across sessions, can set random seed box labeled random seed. example, set 1236. entered information experiment left side panel, click Run! button run design.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/crd.html","id":"outputs","dir":"Articles","previous_headings":"","what":"Outputs","title":"Completely Randomized Design","text":"run completely randomized design FielDHub, several ways display information contained field book.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/crd.html","id":"field-layout","dir":"Articles","previous_headings":"Outputs","what":"Field Layout","title":"Completely Randomized Design","text":"first click run button completely randomized design, FielDHub displays Field Layout tab, shows entries arrangement field. box display, can change layout field. can also display heatmap field changing Type Plot Heatmap. view heatmap, must first simulate experiment described field Simulate! button. pop-window appear can enter variable want simulate along minimum maximum values.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/crd.html","id":"field-book","dir":"Articles","previous_headings":"Outputs","what":"Field Book","title":"Completely Randomized Design","text":"Field Book displays information experimental design table format. contains specific plot number row column address entry, well corresponding treatment plot. table searchable, can filter data relevant columns. simulated data heatmap, additional column variable appears Field Book.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/crd.html","id":"using-the-fieldhub-function-crd","dir":"Articles","previous_headings":"","what":"2. Using the FielDHub function: CRD()","title":"Completely Randomized Design","text":"can run design function FielDHub package, CRD(). can enter information describing design like :","code":"crd <- CRD( t = 15, reps = 6, plotNumber = 101, locationName = \"FARGO\", seed = 1236 )"},{"path":"https://didiermurillof.github.io/FielDHub/articles/crd.html","id":"details-on-the-inputs-entered-in-crd-above","dir":"Articles","previous_headings":"2. Using the FielDHub function: CRD()","what":"Details on the inputs entered in CRD() above","title":"Completely Randomized Design","text":"description inputs used generate design, t = 15 number treatments. reps = 6 number replications treatment. plotNumber = 101 starting plot number. locationNames = \"FARGO\" optional name location. seed = 1234 random seed replicate identical randomizations.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/crd.html","id":"print-crd-output","dir":"Articles","previous_headings":"2. Using the FielDHub function: CRD()","what":"Print crd output","title":"Completely Randomized Design","text":"print summary information object crd, can use generic function print().","code":"print(crd) Completely Randomized Design (CRD) Information on the design parameters: List of 5 $ numberofTreatments: num 15 $ treatments : chr [1:15] \"T1\" \"T2\" \"T3\" \"T4\" ... $ Reps : num 6 $ locationName : chr \"FARGO\" $ seed : num 1236 10 First observations of the data frame with the CRD field book: ID LOCATION PLOT REP TREATMENT 1 1 FARGO 101 4 T9 2 2 FARGO 102 4 T12 3 3 FARGO 103 2 T5 4 4 FARGO 104 3 T9 5 5 FARGO 105 6 T13 6 6 FARGO 106 5 T10 7 7 FARGO 107 5 T5 8 8 FARGO 108 2 T10 9 9 FARGO 109 2 T8 10 10 FARGO 110 3 T10"},{"path":"https://didiermurillof.github.io/FielDHub/articles/crd.html","id":"access-to-crd-output","dir":"Articles","previous_headings":"2. Using the FielDHub function: CRD()","what":"Access to crd output","title":"Completely Randomized Design","text":"CRD() function returns list consisting information displayed output tabs FielDHub app: design information, plot layout, plot numbering, entries list, field book. accessible $ operator, .e.Β crd$layoutRandom crd$fieldBook. crd$fieldBook list containing information every plot field, information location plot treatment plot. seen output , field book columns ID, LOCATION, PLOT, REP, IBLOCK, UNIT, ENTRY, TREATMENT.","code":"field_book <- crd$fieldBook head(crd$fieldBook, 10) ID LOCATION PLOT REP TREATMENT 1 1 FARGO 101 4 T9 2 2 FARGO 102 4 T12 3 3 FARGO 103 2 T5 4 4 FARGO 104 3 T9 5 5 FARGO 105 6 T13 6 6 FARGO 106 5 T10 7 7 FARGO 107 5 T5 8 8 FARGO 108 2 T10 9 9 FARGO 109 2 T8 10 10 FARGO 110 3 T10"},{"path":"https://didiermurillof.github.io/FielDHub/articles/crd.html","id":"plot-the-field-layout","dir":"Articles","previous_headings":"2. Using the FielDHub function: CRD()","what":"Plot the field layout","title":"Completely Randomized Design","text":"plotting layout function coordinates ROW COLUMN, can use generic function plot() follow,","code":"plot(crd)"},{"path":"https://didiermurillof.github.io/FielDHub/articles/diagonal_arrangement.html","id":"single-unreplicated-diagonal-arrangement-design","dir":"Articles","previous_headings":"","what":"Single Unreplicated Diagonal Arrangement Design","title":"Unreplicated Diagonal Arrangement Design","text":"vignette shows generate single multiple unreplicated diagonal arrangement designs using FielDHub Shiny App scripting function diagonal_arrangement() FielDHub R package.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/diagonal_arrangement.html","id":"overview","dir":"Articles","previous_headings":"","what":"Overview","title":"Unreplicated Diagonal Arrangement Design","text":"experiments, insufficient seed quantity field space conduct trials large numbers genotypes, plant breeders must use unreplicated partially replicated experimental designs, like unreplicated designs checks allocated systematic diagonal distribution(Clarke Stefanova 2011). cases, experiment split blocks specified size. allows breeders design field contains multiple different experiments, example, plants various stages maturity. FielDHub includes function run experimental designs, well tabs single multiple diagonal arrangement FielDHub app.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/diagonal_arrangement.html","id":"use-case","dir":"Articles","previous_headings":"","what":"Use Case","title":"Unreplicated Diagonal Arrangement Design","text":"Suppose plant breeding project needs identify superior entries barley. project, preliminary yield trial (PYT) carried 300 genotypes tested one experiment one location unreplicated design. experiment lying field containing 15 rows 22 columns plots. addition, 5 checks included systematic diagonal arrangement across field fill 30 plots representing 9.1% total number experimental plots.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/diagonal_arrangement.html","id":"running-the-shiny-app","dir":"Articles","previous_headings":"","what":"Running the Shiny App","title":"Unreplicated Diagonal Arrangement Design","text":"launch app need run either ","code":"FielDHub::run_app() library(FielDHub) run_app()"},{"path":"https://didiermurillof.github.io/FielDHub/articles/diagonal_arrangement.html","id":"using-the-fieldhub-shiny-app","dir":"Articles","previous_headings":"","what":"1. Using the FielDHub Shiny App","title":"Unreplicated Diagonal Arrangement Design","text":"app running, go Unreplicated Designs > Single Diagonal Arrangement , follow following steps show generate single unreplicated diagonal arrangement design.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/diagonal_arrangement.html","id":"inputs","dir":"Articles","previous_headings":"","what":"Inputs","title":"Unreplicated Diagonal Arrangement Design","text":"selection , means app going generate synthetic data entries names treatment/genotypes based user inputs. selection Yes, entries list must fulfill specific format must .csv file. file must columns ENTRY NAME. ENTRY column must unique integer number entry treatment/genotype. column NAME must unique name identifies treatment/genotype. ENTRY NAME must unique, duplicates allowed. following table, show example entries list format. example entry list 4 checks 8 treatments/genotypes. crucial allocate checks top part file. Enter number entries/treatments Input # Entries box, 300 case. Select 5 drop-Input # Checks box. Since want run experiment 1 location, set Input # Locations 1. Select serpentine cartesian Plot Order Layout. example use serpentine layout. ensure randomizations consistent across sessions, can set random seed box labeled random seed. instance, set 16. Enter name experiment Input Experiment Name box. example, PYT_BARLEY_2022. Enter starting plot number Starting Plot Number box. experiment want plot start 1001. Enter name site/location Input Location box. experiment set site FARGO. case users run experiment multiple locations, name location must enter separate comma, example: FARGO, CASSELTON, MINOT. entered information experiment left side panel, click Run! button run design. prompted select dimensions field list options drop-middle screen box labeled Select dimensions field. case, select 15 x 22. Click Randomize! button randomize experiment set field dimensions see output plots. change inputs left side panel running experiment initially, click Run Randomize buttons , re-run new inputs.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/diagonal_arrangement.html","id":"outputs","dir":"Articles","previous_headings":"","what":"Outputs","title":"Unreplicated Diagonal Arrangement Design","text":"run single diagonal arrangement FielDHub set dimensions field, several ways display information contained field book. first tab, Expt Design Info, shows option change dimensions field re-randomize, well reference guide experiment design.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/diagonal_arrangement.html","id":"input-data","dir":"Articles","previous_headings":"Outputs","what":"Input Data","title":"Unreplicated Diagonal Arrangement Design","text":"second tab, Input Data, can see entries randomization list generated inputs, well table checks number times appear field.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/diagonal_arrangement.html","id":"randomized-field","dir":"Articles","previous_headings":"Outputs","what":"Randomized Field","title":"Unreplicated Diagonal Arrangement Design","text":"Randomized Field tab displays graphical representation randomization entries field specified dimensions. checks colored uniquely, showing number times distributed throughout field. display includes numbered labels rows columns. can copy field table save directly Excel file Copy Excel buttons top. Choose % Checks: drop-box, users can play different options total amount checks field.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/diagonal_arrangement.html","id":"plot-number-field","dir":"Articles","previous_headings":"Outputs","what":"Plot Number Field","title":"Unreplicated Diagonal Arrangement Design","text":"Plot Number Field tab, table display field plots numbered according Plot Order Layout specified, either serpentine cartesian. can see corresponding entries plot number field book. Like Randomized Field tab, can copy table save Excel file Copy Excel buttons.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/diagonal_arrangement.html","id":"field-book","dir":"Articles","previous_headings":"Outputs","what":"Field Book","title":"Unreplicated Diagonal Arrangement Design","text":"Field Book displays information experimental design table format. contains specific plot number row column address entry, well corresponding treatment plot. table searchable, can filter data relevant columns.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/diagonal_arrangement.html","id":"using-the-fieldhub-function-diagonal_arrangement","dir":"Articles","previous_headings":"","what":"2. Using the FielDHub function: diagonal_arrangement()","title":"Unreplicated Diagonal Arrangement Design","text":"can run design function FielDHub package, diagonal_arrangement(). First, need load FielDHub package typing, , can enter information describing design like :","code":"library(FielDHub) single_diag <- diagonal_arrangement( nrows = 15, ncols = 22, lines = 300, checks = 5, l = 1, plotNumber = 1, exptName = \"PYT_BARLEY_2022\", locationNames = \"FARGO\", seed = 16, )"},{"path":"https://didiermurillof.github.io/FielDHub/articles/diagonal_arrangement.html","id":"details-on-the-inputs-entered-in-diagonal_arrangement-above","dir":"Articles","previous_headings":"2. Using the FielDHub function: diagonal_arrangement()","what":"Details on the inputs entered in diagonal_arrangement() above:","title":"Unreplicated Diagonal Arrangement Design","text":"nrows = 15 number columns field. ncols = 22 number rows field. lines = 300 number genotypes. checks = 5 number checks. l = 1 number locations. plotNumber = 1 starting plot number. exptName = \"PYT_BARLEY_2022\" optional name experiment locationNames = \"FARGO\" optional name location. seed = 16 random seed replicate identical randomizations.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/diagonal_arrangement.html","id":"print-single_diag-object","dir":"Articles","previous_headings":"2. Using the FielDHub function: diagonal_arrangement()","what":"Print single_diag object","title":"Unreplicated Diagonal Arrangement Design","text":"print summary information object single_diag, can use generic function print().","code":"print(single_diag) Un-replicated Diagonal Arrangement Design Information on the design parameters: List of 11 $ rows : num 15 $ columns : num 22 $ treatments : int 300 $ checks : int 5 $ entry_checks :List of 1 ..$ : int [1:5] 1 2 3 4 5 $ rep_checks :List of 1 ..$ : num [1:5] 6 6 6 6 6 $ locations : num 1 $ planter : chr \"serpentine\" $ percent_checks: chr \"9.1%\" $ fillers : num 0 $ seed : num 16 10 First observations of the data frame with the diagonal_arrangement field book: ID EXPT LOCATION YEAR PLOT ROW COLUMN CHECKS ENTRY TREATMENT 1 1 PYT_BARLEY_2022 FARGO 2024 1 1 1 0 152 Gen-152 2 2 PYT_BARLEY_2022 FARGO 2024 2 1 2 0 38 Gen-38 3 3 PYT_BARLEY_2022 FARGO 2024 3 1 3 0 285 Gen-285 4 4 PYT_BARLEY_2022 FARGO 2024 4 1 4 0 226 Gen-226 5 5 PYT_BARLEY_2022 FARGO 2024 5 1 5 0 215 Gen-215 6 6 PYT_BARLEY_2022 FARGO 2024 6 1 6 0 272 Gen-272 7 7 PYT_BARLEY_2022 FARGO 2024 7 1 7 0 91 Gen-91 8 8 PYT_BARLEY_2022 FARGO 2024 8 1 8 0 126 Gen-126 9 9 PYT_BARLEY_2022 FARGO 2024 9 1 9 0 289 Gen-289 10 10 PYT_BARLEY_2022 FARGO 2024 10 1 10 0 248 Gen-248"},{"path":"https://didiermurillof.github.io/FielDHub/articles/diagonal_arrangement.html","id":"access-to-single_diag-object","dir":"Articles","previous_headings":"2. Using the FielDHub function: diagonal_arrangement()","what":"Access to single_diag object","title":"Unreplicated Diagonal Arrangement Design","text":"function diagonal_arrangement() returns list consisting information displayed output tabs FielDHub app: design information, plot layout, plot numbering, entries list, field book. accessible $ operator, .e.Β single_diag$layoutRandom single_diag$fieldBook. single_diag$fieldBook data frame containing information every plot field, information location plot treatment plot. seen output , field book columns ID, EXPT, LOCATION, YEAR, PLOT, ROW, COLUMN, CHECKS, ENTRY, TREATMENT. Let us see first 10 rows field book experiment.","code":"field_book <- single_diag$fieldBook head(field_book, 10) ID EXPT LOCATION YEAR PLOT ROW COLUMN CHECKS ENTRY TREATMENT 1 1 PYT_BARLEY_2022 FARGO 2024 1 1 1 0 152 Gen-152 2 2 PYT_BARLEY_2022 FARGO 2024 2 1 2 0 38 Gen-38 3 3 PYT_BARLEY_2022 FARGO 2024 3 1 3 0 285 Gen-285 4 4 PYT_BARLEY_2022 FARGO 2024 4 1 4 0 226 Gen-226 5 5 PYT_BARLEY_2022 FARGO 2024 5 1 5 0 215 Gen-215 6 6 PYT_BARLEY_2022 FARGO 2024 6 1 6 0 272 Gen-272 7 7 PYT_BARLEY_2022 FARGO 2024 7 1 7 0 91 Gen-91 8 8 PYT_BARLEY_2022 FARGO 2024 8 1 8 0 126 Gen-126 9 9 PYT_BARLEY_2022 FARGO 2024 9 1 9 0 289 Gen-289 10 10 PYT_BARLEY_2022 FARGO 2024 10 1 10 0 248 Gen-248"},{"path":"https://didiermurillof.github.io/FielDHub/articles/diagonal_arrangement.html","id":"plot-field-layout","dir":"Articles","previous_headings":"2. Using the FielDHub function: diagonal_arrangement()","what":"Plot field layout","title":"Unreplicated Diagonal Arrangement Design","text":"plotting layout function coordinates ROW COLUMN field book object can use generic function plot() follow, figure shows map experiment randomized single unreplicated diagonal arrangement design. Gray plots represent unreplicated treatments, distinctively colored check plots replicated throughout field systematic diagonal arrangement.","code":"plot(single_diag)"},{"path":"https://didiermurillof.github.io/FielDHub/articles/diagonal_arrangement.html","id":"multiple-unreplicated-diagonal-arrangement-design","dir":"Articles","previous_headings":"","what":"Multiple Unreplicated Diagonal Arrangement Design","title":"Unreplicated Diagonal Arrangement Design","text":"Now, show generate kind unreplicated design multiple experiments field.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/diagonal_arrangement.html","id":"use-case-1","dir":"Articles","previous_headings":"","what":"Use Case","title":"Unreplicated Diagonal Arrangement Design","text":"plant breeding project needs test 300 genotypes divided among three different experiments amounts 100, 120, 80 respectively. experiment represents different stages maturity. 3 experiments lying field containing 15 rows 22 columns plots. addition, 5 checks included systematic diagonal arrangement across experiments fill 30 plots representing 9.1% total number experimental plots. FielDHub can perform randomization design problem explained . can solved either app diagonal_arrangement() function.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/diagonal_arrangement.html","id":"using-the-fieldhub-shiny-app-1","dir":"Articles","previous_headings":"","what":"1. Using the FielDHub Shiny App","title":"Unreplicated Diagonal Arrangement Design","text":"generate multiple unreplicated diagonal arrangement design using FielDHub app: First, go Unreplicated Designs > Multiple Diagonal Arrangement , follow following steps show generate kind design.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/diagonal_arrangement.html","id":"inputs-1","dir":"Articles","previous_headings":"","what":"Inputs","title":"Unreplicated Diagonal Arrangement Design","text":"selection , means app going generate synthetic data entries names treatment/genotypes based user inputs. selection Yes, entries list must fulfill specific format must .csv file. file must columns ENTRY NAME. ENTRY column must unique entry integer number treatment/genotype. column NAME must unique name identifies treatment/genotype. ENTRY NAME must unique, duplicates allowed. following table, show example entries list format. example entry list four checks 8 treatments/genotypes. crucial allocate checks top part file. Note: wish create multiple blocks different sizes imported entries list, example, block size 80, 90, 100 plots, FielDHub read imported entries list checks, 80 entries first block, 90 entries second block, 100 entries last block. Select checkbox option Use entries across experiments purpose make replications instead testing different experiments. Checking option requires size blocks. example, testing 100 treatments across 3 blocks require set 300 Input # Entries 100, 100, 100 input Input # Entries per Expt. case keep unchecked option. Enter total number entries/treatments Input # Entries box, 300 case. Enter number entries/treatments experiment separate comma Input # Entries per Expt box, 100, 120, 80 case. Select 5 drop-Input # Checks box. Since want run experiment 1 location, set Input # Locations 1. Select Row Column Blocks Layout:. example set Row experiments/blocks layout. Select serpentine cartesian Plot Order Layout. example set serpentine layout. Enter starting plot number experiment Starting Plot Number box. experiment want plot start 1, 1001, 2001 experiment. app also allows setting one number experiments. example, plot number start 10. Enter name experiment Input Experiment Name box. example, MATURITY1, MATURITY2, MATURITY3. ensure randomization consistent across sessions, can set random seed box labeled random seed. instance, set 17. Enter name site/location Input Location box. experiment set site FARGO. case users run experiment multiple locations, name location must enter separate comma, example: FARGO, CASSELTON, MINOT. entered information experiment left side panel, click Run! button run design. prompted select dimensions field list options drop-middle screen box labeled Select dimensions field. case, select 15 x 22. Click Randomize! button randomize experiment set field dimensions see output plots. change inputs left side panel running experiment initially, click Run Randomize buttons , re-run new inputs.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/diagonal_arrangement.html","id":"outputs-1","dir":"Articles","previous_headings":"","what":"Outputs","title":"Unreplicated Diagonal Arrangement Design","text":"run single diagonal arrangement FielDHub set dimensions field, several ways display information contained field book. first tab, Expt Design Info, shows option change dimensions field re-randomize, well reference guide experiment design.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/diagonal_arrangement.html","id":"input-data-1","dir":"Articles","previous_headings":"Outputs","what":"Input Data","title":"Unreplicated Diagonal Arrangement Design","text":"second tab, Input Data, can see entries randomization list generated inputs, well table checks number times appear field.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/diagonal_arrangement.html","id":"randomized-field-1","dir":"Articles","previous_headings":"Outputs","what":"Randomized Field","title":"Unreplicated Diagonal Arrangement Design","text":"Randomized Field tab displays graphical representation randomization entries field specified dimensions. checks colored uniquely, showing number times distributed throughout field. display includes numbered labels rows columns. can copy field table save directly Excel file Copy Excel buttons top. tab Choose % Checks: box users can play different options total amount checks field.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/diagonal_arrangement.html","id":"plot-number-field-1","dir":"Articles","previous_headings":"Outputs","what":"Plot Number Field","title":"Unreplicated Diagonal Arrangement Design","text":"Plot Number Field tab, table display field plots numbered according Plot Order Layout specified, either serpentine cartesian. can see corresponding entries plot number field book. Like Randomized field tab, can copy table save Excel file Copy Excel buttons.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/diagonal_arrangement.html","id":"field-book-1","dir":"Articles","previous_headings":"Outputs","what":"Field Book","title":"Unreplicated Diagonal Arrangement Design","text":"Field Book displays information experimental design table format. contains specific plot number row column address entry, well corresponding treatment plot. table searchable, can filter data relevant columns.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/diagonal_arrangement.html","id":"using-the-fieldhub-function-diagonal_arrangement-1","dir":"Articles","previous_headings":"","what":"2. Using the FielDHub function: diagonal_arrangement()","title":"Unreplicated Diagonal Arrangement Design","text":"variation single diagonal arrangement included diagonal_arrangement() function multiple diagonal arrangement, experiment split blocks specified size.","code":"multi_diag <- diagonal_arrangement( nrows = 15, ncols = 22, lines = 300, kindExpt = \"DBUDC\", blocks = c(100,120,80), checks = 5, l = 1, plotNumber = c(1, 1001, 2001), exptName = c(\"MATURITY1\", \"MATURITY2\", \"MATURITY3\"), locationNames = \"FARGO\", seed = 17 )"},{"path":"https://didiermurillof.github.io/FielDHub/articles/diagonal_arrangement.html","id":"details-on-the-inputs-entered-in-diagonal_arrangement-above-1","dir":"Articles","previous_headings":"2. Using the FielDHub function: diagonal_arrangement()","what":"Details on the inputs entered in diagonal_arrangement() above:","title":"Unreplicated Diagonal Arrangement Design","text":"description inputs used generate design, nrows = 15 number columns field. ncols = 22 number rows field. lines = 300 number genotypes. kindExpt = \"DBUDC\" option randomize multiple experiments blocks = c(100,120,80) blocks multiple arrangement. checks = 5 number checks. l = 1 number locations. plotNumber = c(1, 1001, 2001) starting plot number experiment. just one number well. exptName = c(\"MATURITY1\", \"MATURITY2\", \"MATURITY3\") optional name experiment. locationNames = \"FARGO\" optional name location. seed = 17 random seed replicate identical randomizations.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/diagonal_arrangement.html","id":"print-multi_diag-object","dir":"Articles","previous_headings":"2. Using the FielDHub function: diagonal_arrangement()","what":"Print multi_diag object","title":"Unreplicated Diagonal Arrangement Design","text":"printing summary information object multi_diag can use generic function print()","code":"print(multi_diag) Un-replicated Diagonal Arrangement Design Information on the design parameters: List of 11 $ rows : num 15 $ columns : num 22 $ treatments : num [1:3] 100 120 80 $ checks : int 5 $ entry_checks :List of 1 ..$ : int [1:5] 1 2 3 4 5 $ rep_checks :List of 1 ..$ : num [1:5] 7 5 7 5 6 $ locations : num 1 $ planter : chr \"serpentine\" $ percent_checks: chr \"9.1%\" $ fillers : num 0 $ seed : num 17 10 First observations of the data frame with the diagonal_arrangement field book: ID EXPT LOCATION YEAR PLOT ROW COLUMN CHECKS ENTRY TREATMENT 1 1 MATURITY1 FARGO 2024 1 1 1 0 104 Gen-104 2 2 MATURITY1 FARGO 2024 2 1 2 0 65 Gen-65 3 3 MATURITY1 FARGO 2024 3 1 3 0 70 Gen-70 4 4 MATURITY1 FARGO 2024 4 1 4 0 8 Gen-8 5 5 MATURITY1 FARGO 2024 5 1 5 0 51 Gen-51 6 6 MATURITY1 FARGO 2024 6 1 6 0 17 Gen-17 7 7 MATURITY1 FARGO 2024 7 1 7 0 11 Gen-11 8 8 MATURITY1 FARGO 2024 8 1 8 0 6 Gen-6 9 9 MATURITY1 FARGO 2024 9 1 9 0 53 Gen-53 10 10 MATURITY1 FARGO 2024 10 1 10 0 50 Gen-50"},{"path":"https://didiermurillof.github.io/FielDHub/articles/diagonal_arrangement.html","id":"access-to-multi_diag-object","dir":"Articles","previous_headings":"2. Using the FielDHub function: diagonal_arrangement()","what":"Access to multi_diag object","title":"Unreplicated Diagonal Arrangement Design","text":"function diagonal_arrangement() returns list consisting information displayed output tabs FielDHub app: design information, plot layout, plot numbering, entries list, field book. accessible $ operator, .e.Β multi_diag$layoutRandom multi_diag$fieldBook. multi_diag$fieldBook data frame containing information every plot field, information location plot treatment plot. seen output , field book columns ID, EXPT, LOCATION, YEAR, PLOT, ROW, COLUMN, CHECKS, ENTRY, TREATMENT. Let us see first 10 rows field book experiment.","code":"field_book <- multi_diag$fieldBook head(field_book, 10) ID EXPT LOCATION YEAR PLOT ROW COLUMN CHECKS ENTRY TREATMENT 1 1 MATURITY1 FARGO 2024 1 1 1 0 104 Gen-104 2 2 MATURITY1 FARGO 2024 2 1 2 0 65 Gen-65 3 3 MATURITY1 FARGO 2024 3 1 3 0 70 Gen-70 4 4 MATURITY1 FARGO 2024 4 1 4 0 8 Gen-8 5 5 MATURITY1 FARGO 2024 5 1 5 0 51 Gen-51 6 6 MATURITY1 FARGO 2024 6 1 6 0 17 Gen-17 7 7 MATURITY1 FARGO 2024 7 1 7 0 11 Gen-11 8 8 MATURITY1 FARGO 2024 8 1 8 0 6 Gen-6 9 9 MATURITY1 FARGO 2024 9 1 9 0 53 Gen-53 10 10 MATURITY1 FARGO 2024 10 1 10 0 50 Gen-50"},{"path":"https://didiermurillof.github.io/FielDHub/articles/diagonal_arrangement.html","id":"plot-field-layout-1","dir":"Articles","previous_headings":"2. Using the FielDHub function: diagonal_arrangement()","what":"Plot field layout","title":"Unreplicated Diagonal Arrangement Design","text":"plotting layout function coordinates ROW COLUMN field book object can use generic function plot() follow, figure shows map experiment randomized multiple unreplicated diagonal arrangement design. Gray, salmon, pink shade blocks unreplicated experiments, distinctively colored check plots replicated throughout field systematic diagonal arrangement.","code":"plot(multi_diag)"},{"path":[]},{"path":"https://didiermurillof.github.io/FielDHub/articles/full_factorial.html","id":"using-the-fieldhub-shiny-app","dir":"Articles","previous_headings":"","what":"1. Using the FielDHub Shiny App","title":"Full Factorial Design","text":"launch app need run either app running, go Designs > Full Factorial Designs , follow following steps show generate kind design example set 3 treatments levels 3, 3, 2 . run experiment 3 times.","code":"FielDHub::run_app() library(FielDHub) run_app()"},{"path":"https://didiermurillof.github.io/FielDHub/articles/full_factorial.html","id":"inputs","dir":"Articles","previous_headings":"","what":"Inputs","title":"Full Factorial Design","text":"selection , means app going generate synthetic data entries names treatment based user inputs. selection Yes, entries list must fulfill specific format must .csv file. file must two columns: FACTORS LEVEL. Containing list unique names identify treatment level. Duplicate values allowed, entries must unique. following table, show example entries list format. example entry list three treatments/factors, 3, 3 2 levels . Choose whether use factorial design RCBD CRD Select Factorial Design Type box. Set RCBD. Set number entries factor comma separated list Input # Entries Factor box. want example experiment 3 factors 3, 3, 2 levels respectively, enter 3, 3, 2 box. Set number replications squares Input # Full Reps box. Set 3. Enter number locations Input # Locations. Set 1. Enter starting plot number Starting Plot Number box. experiment multiple locations, must enter comma separated list numbers length number locations input valid. case, set 101. Optionally, may enter name location experiment Input Location box. Select serpentine cartesian Plot Order Layout. example use default serpentine layout. designs, can set random seed box labeled random seed. example, set 1239. entered information experiment left side panel, click Run! button run design.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/full_factorial.html","id":"outputs","dir":"Articles","previous_headings":"","what":"Outputs","title":"Full Factorial Design","text":"run full factorial design FielDHub, several ways display information contained field book.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/full_factorial.html","id":"field-layout","dir":"Articles","previous_headings":"Outputs","what":"Field Layout","title":"Full Factorial Design","text":"first click run button full factorial design, FielDHub displays Field Layout tab, shows entries arrangement field. box display, can change layout field change location displayed. can also display heatmap field changing Type Plot Heatmap. view heatmap, must first simulate experiment described field Simulate! button. pop-window appear can enter variable want simulate along minimum maximum values.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/full_factorial.html","id":"field-book","dir":"Articles","previous_headings":"Outputs","what":"Field Book","title":"Full Factorial Design","text":"Field Book displays information experimental design table format. contains specific plot number row column address entry, well corresponding treatment plot. table searchable, can filter data relevant columns. simulated data heatmap, additional column variable appears Field Book.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/full_factorial.html","id":"using-the-fieldhub-function-full_factorial","dir":"Articles","previous_headings":"","what":"2. Using the FielDHub function: full_factorial()","title":"Full Factorial Design","text":"can run design function FielDHub package, full_factorial(). First, need load FielDHub package typing, , can enter information describing design like :","code":"library(FielDHub) factorial <- full_factorial( setfactors = c(3,3,2), reps = 3, l = 1, type = 2, plotNumber = 101, planter = \"serpentine\", locationNames = \"FARGO\", seed = 1239 )"},{"path":"https://didiermurillof.github.io/FielDHub/articles/full_factorial.html","id":"details-on-the-inputs-entered-in-full_factorial-above","dir":"Articles","previous_headings":"2. Using the FielDHub function: full_factorial()","what":"Details on the inputs entered in full_factorial() above","title":"Full Factorial Design","text":"description inputs used generate design, setfactors = c(3,3,2) levels factor. reps = 3 number replications treatment. l = 1 number locations. type = 2 means CRD RCBD, 1 2 respectively. plotNumber = 101 starting plot number. planter = \"serpentine\" order layout. locationNames = \"FARGO\" optional name location. seed = 1239 random seed replicate identical randomizations.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/full_factorial.html","id":"print-factorial-object","dir":"Articles","previous_headings":"2. Using the FielDHub function: full_factorial()","what":"Print factorial object","title":"Full Factorial Design","text":"","code":"print(factorial) Full Factorial Design Information on the design parameters: List of 9 $ factors : chr [1:3] \"A\" \"B\" \"C\" $ levels : int [1:8] 0 1 2 0 1 2 0 1 $ runs : int 18 $ all_treatments :'data.frame': 18 obs. of 3 variables: ..$ A: int [1:18] 0 1 2 0 1 2 0 1 2 0 ... ..$ B: int [1:18] 0 0 0 1 1 1 2 2 2 0 ... ..$ C: int [1:18] 0 0 0 0 0 0 0 0 0 1 ... $ reps : num 3 $ locations : num 1 $ location_names : chr \"FARGO\" $ kind : chr \"RCBD\" $ levels_each_factor: num [1:3] 3 3 2 10 First observations of the data frame with the full_factorial field book: ID LOCATION PLOT REP FACTOR_A FACTOR_B FACTOR_C TRT_COMB 1 1 FARGO 101 1 0 1 0 0*1*0 2 2 FARGO 102 1 1 1 0 1*1*0 3 3 FARGO 103 1 2 1 0 2*1*0 4 4 FARGO 104 1 2 1 1 2*1*1 5 5 FARGO 105 1 2 2 0 2*2*0 6 6 FARGO 106 1 1 0 1 1*0*1 7 7 FARGO 107 1 0 0 1 0*0*1 8 8 FARGO 108 1 1 2 0 1*2*0 9 9 FARGO 109 1 0 2 0 0*2*0 10 10 FARGO 110 1 0 1 1 0*1*1"},{"path":"https://didiermurillof.github.io/FielDHub/articles/full_factorial.html","id":"access-to-factorial-object","dir":"Articles","previous_headings":"2. Using the FielDHub function: full_factorial()","what":"Access to factorial object","title":"Full Factorial Design","text":"full_factorial() function returns list consisting information displayed output tabs FielDHub app: design information, plot layout, plot numbering, entries list, field book. accessible $ operator, .e.Β factorial$layoutRandom factorial$fieldBook. factorial$fieldBook list containing information every plot field, information location plot treatment plot. seen output , field book columns ID, LOCATION, PLOT, REP, TRT_COMB, columns factor individually.","code":"field_book <- factorial$fieldBook head(factorial$fieldBook, 10) ID LOCATION PLOT REP FACTOR_A FACTOR_B FACTOR_C TRT_COMB 1 1 FARGO 101 1 0 1 0 0*1*0 2 2 FARGO 102 1 1 1 0 1*1*0 3 3 FARGO 103 1 2 1 0 2*1*0 4 4 FARGO 104 1 2 1 1 2*1*1 5 5 FARGO 105 1 2 2 0 2*2*0 6 6 FARGO 106 1 1 0 1 1*0*1 7 7 FARGO 107 1 0 0 1 0*0*1 8 8 FARGO 108 1 1 2 0 1*2*0 9 9 FARGO 109 1 0 2 0 0*2*0 10 10 FARGO 110 1 0 1 1 0*1*1"},{"path":"https://didiermurillof.github.io/FielDHub/articles/full_factorial.html","id":"plot-the-field-layout","dir":"Articles","previous_headings":"2. Using the FielDHub function: full_factorial()","what":"Plot the field layout","title":"Full Factorial Design","text":"plotting layout function coordinates ROW COLUMN, can use generic function plot() follow,","code":"plot(factorial)"},{"path":"https://didiermurillof.github.io/FielDHub/articles/incomplete_blocks.html","id":"using-the-fieldhub-shiny-app","dir":"Articles","previous_headings":"","what":"1. Using the FielDHub Shiny App","title":"Incomplete Block Design","text":"launch app need run either app running, go Designs > Incomplete Block Design (IBD) , follow following steps show generate kind design example 28 treatments 4 reps. run experiment just one location.","code":"FielDHub::run_app() library(FielDHub) run_app()"},{"path":"https://didiermurillof.github.io/FielDHub/articles/incomplete_blocks.html","id":"inputs","dir":"Articles","previous_headings":"","what":"Inputs","title":"Incomplete Block Design","text":"selection , means app going generate synthetic data entries names treatment/genotypes based user inputs. selection Yes, entries list must fulfill specific format must .csv file. file must columns ENTRY NAME. ENTRY column must unique entry integer number treatment/genotype. column NAME must unique name identifies treatment/genotype. ENTRY NAME must unique, duplicates allowed. following table, show example entries list format. example entry list 12 treatments. Input number treatments Input # Treatments box. alpha lattice design, number treatments must composite number. case, Set 28. Select number replications treatments Input # Full Reps box. Set 4. Set number plots incomplete block Input # Plots per IBlock box. Set 4. Enter number locations Input # Locations. run experiment single location, set 1. Select serpentine cartesian Plot Order Layout. example use default cartesian layout. Enter starting plot number Starting Plot Number box. experiment multiple locations, must enter comma separated list numbers length number locations input valid. Set 101. Enter name location experiment Input Location box. multiple locations, name must comma separated list. Set \"FARGO\". ensure randomizations consistent across sessions, can set random seed box labeled random seed. example, set 1243. entered information experiment left side panel, click Run! button run design.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/incomplete_blocks.html","id":"outputs","dir":"Articles","previous_headings":"","what":"Outputs","title":"Incomplete Block Design","text":"run incomplete block design FielDHub, several ways display information contained field book.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/incomplete_blocks.html","id":"field-layout","dir":"Articles","previous_headings":"Outputs","what":"Field Layout","title":"Incomplete Block Design","text":"first click run button incomplete block design, FielDHub displays Field Layout tab, shows entries arrangement field. box display, can change layout field. can also display heatmap field changing Type Plot Heatmap. view heatmap, must first simulate experiment described field Simulate! button. pop-window appear can enter variable want simulate along minimum maximum values.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/incomplete_blocks.html","id":"field-book","dir":"Articles","previous_headings":"Outputs","what":"Field Book","title":"Incomplete Block Design","text":"Field Book displays information experimental design table format. contains specific plot number row column address entry, well corresponding treatment plot. table searchable, can filter data relevant columns. simulated data heatmap, additional column variable appears Field Book.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/incomplete_blocks.html","id":"using-the-fieldhub-function-incomplete_blocks","dir":"Articles","previous_headings":"","what":"2. Using the FielDHub function: incomplete_blocks()","title":"Incomplete Block Design","text":"can run design function FielDHub package, incomplete_blocks(). First, need load FielDHub package typing, , can enter information describing design like :","code":"library(FielDHub) ibd <- incomplete_blocks( t = 28, r = 4, k = 4, l = 1, seed = 1243 )"},{"path":"https://didiermurillof.github.io/FielDHub/articles/incomplete_blocks.html","id":"details-on-the-inputs-entered-in-incomplete_blocks-above","dir":"Articles","previous_headings":"2. Using the FielDHub function: incomplete_blocks()","what":"Details on the inputs entered in incomplete_blocks() above","title":"Incomplete Block Design","text":"description inputs used generate design, t = 28 number treatments. r=4 number replicates. k = 4 number plots per incomplete block. l = 1 number locations plotNumber = 101 starting plot number. locationNames = \"FARGO\" optional name location. seed = 1243 random seed replicate identical randomizations.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/incomplete_blocks.html","id":"print-ibd-object","dir":"Articles","previous_headings":"2. Using the FielDHub function: incomplete_blocks()","what":"Print ibd object","title":"Incomplete Block Design","text":"","code":"print(ibd) Incomplete Blocks Design Efficiency of design: Level Blocks D-Efficiency A-Efficiency A-Bound 1 1 4 1.0000000 1.0000000 1.0000000 2 2 28 0.7603326 0.7431887 0.7470356 Information on the design parameters: List of 7 $ Reps : num 4 $ iBlocks : num 7 $ NumberTreatments: num 28 $ NumberLocations : num 1 $ Locations : int 1 $ seed : num 1243 $ lambda : num 0.444 10 First observations of the data frame with the incomplete_blocks field book: ID LOCATION PLOT REP IBLOCK UNIT ENTRY TREATMENT 1 1 1 101 1 1 1 6 G-6 2 2 1 102 1 1 2 11 G-11 3 3 1 103 1 1 3 15 G-15 4 4 1 104 1 1 4 23 G-23 5 5 1 105 1 2 1 17 G-17 6 6 1 106 1 2 2 7 G-7 7 7 1 107 1 2 3 28 G-28 8 8 1 108 1 2 4 13 G-13 9 9 1 109 1 3 1 12 G-12 10 10 1 110 1 3 2 20 G-20"},{"path":"https://didiermurillof.github.io/FielDHub/articles/incomplete_blocks.html","id":"access-to-ibd-object","dir":"Articles","previous_headings":"2. Using the FielDHub function: incomplete_blocks()","what":"Access to ibd object","title":"Incomplete Block Design","text":"incomplete_blocks() function returns list consisting information displayed output tabs FielDHub app: design information, plot layout, plot numbering, entries list, field book. accessible $ operator, .e.Β ibd$layoutRandom ibd$fieldBook. ibd$fieldBook list containing information every plot field, information location plot treatment plot. seen output , field book columns ID, LOCATION, PLOT, REP, IBLOCK, UNIT, ENTRY, TREATMENT.","code":"field_book <- ibd$fieldBook head(ibd$fieldBook, 10) ID LOCATION PLOT REP IBLOCK UNIT ENTRY TREATMENT 1 1 1 101 1 1 1 6 G-6 2 2 1 102 1 1 2 11 G-11 3 3 1 103 1 1 3 15 G-15 4 4 1 104 1 1 4 23 G-23 5 5 1 105 1 2 1 17 G-17 6 6 1 106 1 2 2 7 G-7 7 7 1 107 1 2 3 28 G-28 8 8 1 108 1 2 4 13 G-13 9 9 1 109 1 3 1 12 G-12 10 10 1 110 1 3 2 20 G-20"},{"path":"https://didiermurillof.github.io/FielDHub/articles/incomplete_blocks.html","id":"plot-the-field-layout","dir":"Articles","previous_headings":"2. Using the FielDHub function: incomplete_blocks()","what":"Plot the field layout","title":"Incomplete Block Design","text":"plotting layout function coordinates ROW COLUMN, can use generic function plot() follow,","code":"plot(ibd)"},{"path":"https://didiermurillof.github.io/FielDHub/articles/latin_square.html","id":"using-the-fieldhub-shiny-app","dir":"Articles","previous_headings":"","what":"1. Using the FielDHub Shiny App","title":"Latin Square Design","text":"launch app need run either app running, go Designs > Latin Square Design , follow following steps show generate kind design example 5 treatments 2 reps.","code":"FielDHub::run_app() library(FielDHub) run_app()"},{"path":"https://didiermurillof.github.io/FielDHub/articles/latin_square.html","id":"inputs","dir":"Articles","previous_headings":"","what":"Inputs","title":"Latin Square Design","text":"selection , means app going generate synthetic data entries names treatment based user inputs. selection Yes, entries list must fulfill specific format must .csv file. file must three columns: ROW, COLUMN TREATMENT. columns contain list unique names identify treatment. Duplicate values allowed, entries must unique. following table, show example entries list format. example entry list 5 treatments. Input number treatments Input # Treatments box. alpha lattice design, number treatments must composite number. Select number replications treatments Input # Full Reps box. number treatments number full reps set dimensions field. Select serpentine cartesian Plot Order Layout. example use default serpentine layout. Enter starting plot number Starting Plot Number box. experiment multiple locations, must enter comma separated list numbers length number locations input valid. Enter name location experiment Input Location box. completely randomized design can run single location time. ensure randomizations consistent across sessions, can set random seed box labeled random seed. example, set 123. entered information experiment left side panel, click Run! button run design.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/latin_square.html","id":"outputs","dir":"Articles","previous_headings":"","what":"Outputs","title":"Latin Square Design","text":"run Latin square design FielDHub, several ways display information contained field book.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/latin_square.html","id":"field-layout","dir":"Articles","previous_headings":"Outputs","what":"Field Layout","title":"Latin Square Design","text":"first click run button Latin square design, FielDHub displays Field Layout tab, shows entries arrangement field. box display, can change layout field. can also display heatmap field changing Type Plot Heatmap. view heatmap, must first simulate experiment described field Simulate! button. pop-window appear can enter variable want simulate along minimum maximum values.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/latin_square.html","id":"field-book","dir":"Articles","previous_headings":"Outputs","what":"Field Book","title":"Latin Square Design","text":"Field Book displays information experimental design table format. contains specific plot number row column address entry, well corresponding treatment plot. table searchable, can filter data relevant columns. simulated data heatmap, additional column variable appears field book.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/latin_square.html","id":"using-the-fieldhub-function-latin_square","dir":"Articles","previous_headings":"","what":"2. Using the FielDHub function: latin_square()","title":"Latin Square Design","text":"can run design function FielDHub package, latin_square(). First, need load FielDHub package typing, , can enter information describing design like :","code":"library(FielDHub) lsd <- latin_square( t = 5, reps = 2, plotNumber = 101, planter = \"serpentine\", seed = 1238 )"},{"path":"https://didiermurillof.github.io/FielDHub/articles/latin_square.html","id":"details-on-the-inputs-entered-in-latin_square-above","dir":"Articles","previous_headings":"2. Using the FielDHub function: latin_square()","what":"Details on the inputs entered in latin_square() above","title":"Latin Square Design","text":"t = 5 number treatments. reps = 2 number replications (squares). plotNumber = 101 starting plot number. planter = \"cartesian\" plot order layout. locationNames = \"FARGO\" optional name location. seed = 1238 random seed replicate identical randomizations.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/latin_square.html","id":"print-lsd-object","dir":"Articles","previous_headings":"2. Using the FielDHub function: latin_square()","what":"Print lsd object","title":"Latin Square Design","text":"","code":"print(lsd) Latin Square Design: Information on the design parameters: List of 4 $ treatments : int 5 $ squares : num 2 $ locationName: NULL $ seed : num 1238 10 First observations of the data frame with the latin_square field book: ID LOCATION PLOT SQUARE ROW COLUMN TREATMENT 1 1 1 101 1 Row 1 Column 1 T5 2 2 1 102 1 Row 1 Column 2 T1 3 3 1 103 1 Row 1 Column 3 T2 4 4 1 104 1 Row 1 Column 4 T4 5 5 1 105 1 Row 1 Column 5 T3 6 6 1 110 1 Row 2 Column 1 T4 7 7 1 109 1 Row 2 Column 2 T2 8 8 1 108 1 Row 2 Column 3 T3 9 9 1 107 1 Row 2 Column 4 T1 10 10 1 106 1 Row 2 Column 5 T5"},{"path":"https://didiermurillof.github.io/FielDHub/articles/latin_square.html","id":"access-to-lsd-object","dir":"Articles","previous_headings":"2. Using the FielDHub function: latin_square()","what":"Access to lsd object","title":"Latin Square Design","text":"latin_square() function returns list consisting information displayed output tabs FielDHub app: design information, plot layout, plot numbering, entries list, field book. accessible $ operator, .e.Β lsd$layoutRandom lsd$fieldBook. lsd$fieldBook list containing information every plot field, information location plot treatment plot. seen output , field book columns ID, LOCATION, PLOT, SQUARE, ROW, COLUMN, TREATMENT.","code":"field_book <- lsd$fieldBook head(lsd$fieldBook, 10) ID LOCATION PLOT SQUARE ROW COLUMN TREATMENT 1 1 1 101 1 Row 1 Column 1 T5 2 2 1 102 1 Row 1 Column 2 T1 3 3 1 103 1 Row 1 Column 3 T2 4 4 1 104 1 Row 1 Column 4 T4 5 5 1 105 1 Row 1 Column 5 T3 6 6 1 110 1 Row 2 Column 1 T4 7 7 1 109 1 Row 2 Column 2 T2 8 8 1 108 1 Row 2 Column 3 T3 9 9 1 107 1 Row 2 Column 4 T1 10 10 1 106 1 Row 2 Column 5 T5"},{"path":"https://didiermurillof.github.io/FielDHub/articles/latin_square.html","id":"plot-the-field-layout","dir":"Articles","previous_headings":"2. Using the FielDHub function: latin_square()","what":"Plot the field layout","title":"Latin Square Design","text":"plotting layout function coordinates ROW COLUMN, can use generic function plot() follow,","code":"plot(lsd)"},{"path":"https://didiermurillof.github.io/FielDHub/articles/methods.html","id":"print","dir":"Articles","previous_headings":"","what":"print()","title":"Methods in FielDHub","text":"print() function prints design parameters experiment first 10 rows field book. first 10 rows field book saved output function assigned variable.","code":"print(experiment) Randomized Complete Block Design (RCBD): Information on the design parameters: List of 7 $ blocks : num 3 $ number.of.treatments: num 12 $ treatments : chr [1:12] \"T1\" \"T2\" \"T3\" \"T4\" ... $ locations : num 2 $ plotNumber : num [1:6] 1001 1101 1201 2001 2101 ... $ locationNames : chr [1:2] \"A\" \"B\" $ seed : num 123 10 First observations of the data frame with the RCBD field book: ID LOCATION PLOT REP TREATMENT 1 1 A 1001 1 T3 2 2 A 1002 1 T12 3 3 A 1003 1 T10 4 4 A 1004 1 T2 5 5 A 1005 1 T6 6 6 A 1006 1 T11 7 7 A 1007 1 T5 8 8 A 1008 1 T4 9 9 A 1009 1 T9 10 10 A 1010 1 T8"},{"path":"https://didiermurillof.github.io/FielDHub/articles/methods.html","id":"summary","dir":"Articles","previous_headings":"","what":"summary()","title":"Methods in FielDHub","text":"summary() function outputs list design parameters layout randomization plot numbers.","code":"summary(experiment) Randomized Complete Block Design (RCBD): 1. Information on the design parameters: List of 8 $ blocks : num 3 $ number.of.treatments: num 12 $ treatments : chr [1:12] \"T1\" \"T2\" \"T3\" \"T4\" ... $ locations : num 2 $ plotNumber : num [1:6] 1001 1101 1201 2001 2101 ... $ locationNames : chr [1:2] \"A\" \"B\" $ seed : num 123 $ id_design : num 2 2. Layout randomization for each location: $Loc_A Block --Treatments-- [1,] \"1\" \"T3 T12 T10 T2 T6 T11 T5 T4 T9 T8 T1 T7\" [2,] \"2\" \"T11 T5 T3 T9 T4 T1 T7 T12 T10 T2 T6 T8\" [3,] \"3\" \"T9 T3 T4 T1 T11 T7 T5 T10 T8 T2 T12 T6\" $Loc_B Block --Treatments-- [1,] \"1\" \"T9 T12 T10 T7 T3 T4 T5 T6 T8 T2 T1 T11\" [2,] \"2\" \"T5 T8 T2 T1 T9 T3 T11 T12 T7 T6 T4 T10\" [3,] \"3\" \"T12 T4 T6 T8 T10 T9 T1 T2 T7 T5 T3 T11\" 3. Plot numbers layout: $Loc_A [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [1,] 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 [2,] 1112 1111 1110 1109 1108 1107 1106 1105 1104 1103 1102 1101 [3,] 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 $Loc_B [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [1,] 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 [2,] 2112 2111 2110 2109 2108 2107 2106 2105 2104 2103 2102 2101 [3,] 2201 2202 2203 2204 2205 2206 2207 2208 2209 2210 2211 2212 4. Structure of the data frame with the RCBD field book: 'data.frame': 72 obs. of 5 variables: $ ID : int 1 2 3 4 5 6 7 8 9 10 ... $ LOCATION : Factor w/ 2 levels \"A\",\"B\": 1 1 1 1 1 1 1 1 1 1 ... $ PLOT : int 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 ... $ REP : int 1 1 1 1 1 1 1 1 1 1 ... $ TREATMENT: chr \"T3\" \"T12\" \"T10\" \"T2\" ..."},{"path":"https://didiermurillof.github.io/FielDHub/articles/methods.html","id":"plot","dir":"Articles","previous_headings":"","what":"plot()","title":"Methods in FielDHub","text":"plot() function plots field input design, displayed FielDHub. can also saved variable. function parameters location layout, applicable.","code":"plot(experiment, l = 2, layout = 2)"},{"path":"https://didiermurillof.github.io/FielDHub/articles/multi_location_prep.html","id":"overview","dir":"Articles","previous_headings":"","what":"Overview","title":"Optimized Multi-Location P-rep Design","text":"Partially replicated (p-rep) designs commonly employed early generation field trials. type design characterized replication portion entries, remaining entries appearing experiment. Commonly, part treatments reps due arbitrary decision research, also cases, due technical reasons. replication ratio typically 1:4 (Cullis 2006), means portion treatment repeated twice p = 25%. However, design can adapted meet specific needs adjusting values pp level replication. example, standard varieties (checks) may included higher levels replication test lines. FielDHub, optimized multi-location p-rep design employs principles incomplete block designs (IBD) determine distribution replicated non-replicated treatments across multiple locations.","code":""},{"path":[]},{"path":"https://didiermurillof.github.io/FielDHub/articles/multi_location_prep.html","id":"across-location","dir":"Articles","previous_headings":"Optimization","what":"Across Location","title":"Optimized Multi-Location P-rep Design","text":"function multi_location_prep() uses incomplete blocks design approach (Edmondson 2020) optimize allocation replicated un-replicated treatments across environments.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/multi_location_prep.html","id":"within-location","dir":"Articles","previous_headings":"Optimization","what":"Within Location","title":"Optimized Multi-Location P-rep Design","text":"partially replicated (p-rep) design location undergoes optimization process involves following procedure: Given matrix XX integers (p-rep design within location), want ensure distance two occurrences treatment least distance dd. specifically, want modify XX ensure treatments appear twice within distance less dd resulting matrix. goal optimization process find modified matrix satisfies constraint maximizing measure deviation original matrix XX. case, measure deviation pairwise Euclidean distance occurrences treatment. process done function swap_pairs() uses heuristic algorithm starts distance d=3d = 3 pairs occurrences treatment, increases distance 11 repeats process either algorithm longer converges maximum number iterations reached. algorithm works first identifying pairs occurrences treatment closer dd. pair, function selects random occurrence different integer least dd away, swaps two occurrences. process repeated swaps can made increase pairwise Euclidean distances occurrences treatment.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/multi_location_prep.html","id":"toy-example","dir":"Articles","previous_headings":"Optimization > Within Location","what":"Toy Example","title":"Optimized Multi-Location P-rep Design","text":"Consider p-rep design ten treatments replicated twice 40 . matrix (field layout) experiment 6 rows 10 columns. X=X = initial p-rep design, notice two instances treatment 5 positioned next . Additionally, treatments 7 9 also situated adjacent cells. suboptimal allocations lead issues inaccurate results analyzing data experiment due short distance replicated treatments likely spatial correlation . following table shows pairwise distances replicated treatments","code":"[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [1,] 21 40 17 25 26 3 11 31 36 6 [2,] 5 5 33 8 48 29 43 23 1 45 [3,] 41 27 38 39 7 28 14 22 24 4 [4,] 4 47 18 7 2 35 6 20 12 46 [5,] 3 15 9 34 49 50 2 10 42 8 [6,] 32 16 19 9 10 13 37 1 44 30 geno Pos1 Pos2 DIST rA cA rB cB 5 5 2 8 1.000000 2 1 2 2 7 7 22 27 1.414214 4 4 3 5 9 9 17 24 1.414214 5 3 6 4 2 2 28 41 2.236068 4 5 5 7 10 10 30 47 3.162278 6 5 5 8 1 1 48 50 4.123106 6 8 2 9 6 6 40 55 4.242641 4 7 1 10 3 3 5 31 6.403124 5 1 1 6 8 8 20 59 6.708204 2 4 5 10 4 4 4 57 9.055385 4 1 3 10"},{"path":"https://didiermurillof.github.io/FielDHub/articles/multi_location_prep.html","id":"swap-pairs","dir":"Articles","previous_headings":"Optimization > Within Location","what":"Swap pairs","title":"Optimized Multi-Location P-rep Design","text":"can improve efficiency design swapping treatments close next using function swap_pairs() FielDHub R package. new matrix optimized p-rep design , distances pairwise treatments , can see, minimum distance algorithm reached 5. means treatments appear twice within distance less 5 resulting prep design. considerable improvement first version p-rep design FielDHub function multi_location_prep() internally optimization process uses function swap_pairs() maximize distance replicated treatments.","code":"library(FielDHub) B <- swap_pairs(X, starting_dist = 3) print(B$optim_design) [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [1,] 8 35 6 2 33 44 3 4 37 6 [2,] 43 30 25 5 39 29 19 11 36 45 [3,] 40 13 38 10 20 28 15 41 10 17 [4,] 1 27 18 31 32 22 24 21 12 5 [5,] 23 47 3 34 49 50 16 46 14 48 [6,] 7 26 2 42 9 1 8 7 4 9 print(B$pairwise_distance) geno Pos1 Pos2 DIST rA cA rB cB 9 9 30 60 5.000000 6 5 6 10 10 10 21 51 5.000000 3 4 3 9 2 2 18 19 5.099020 6 3 1 4 4 4 43 54 5.099020 1 8 6 9 1 1 4 36 5.385165 4 1 6 6 3 3 17 37 5.656854 5 3 1 7 5 5 20 58 6.324555 2 4 4 10 6 6 13 55 7.000000 1 3 1 10 7 7 6 48 7.000000 6 1 6 8 8 8 1 42 7.810250 1 1 6 7"},{"path":"https://didiermurillof.github.io/FielDHub/articles/multi_location_prep.html","id":"use-case-multi-location-p-rep-design","dir":"Articles","previous_headings":"","what":"Use case (Multi-Location P-rep Design)","title":"Optimized Multi-Location P-rep Design","text":"Suppose plant breeding field trial 150 entries tested across five environments, seven replications entry allowed. Additionally, project includes three checks; replicated six times. can generate optimized multi-location partially replicated design using parameters. strategy guarantees treatments present environments different amounts replications. can generate design using FielDHub Shiny app FielDHub multi_location_prep() standalone function R.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/multi_location_prep.html","id":"running-the-shiny-app","dir":"Articles","previous_headings":"","what":"Running the Shiny App","title":"Optimized Multi-Location P-rep Design","text":"launch app need run either ","code":"FielDHub::run_app() library(FielDHub) run_app()"},{"path":"https://didiermurillof.github.io/FielDHub/articles/multi_location_prep.html","id":"using-the-fieldhub-shiny-app","dir":"Articles","previous_headings":"","what":"1. Using the FielDHub Shiny App","title":"Optimized Multi-Location P-rep Design","text":"app running, click tab Partially Replicated Design select Optimized Multi-Location p-rep dropdown. , follow following steps show generate optimized partially replicated design.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/multi_location_prep.html","id":"inputs","dir":"Articles","previous_headings":"","what":"Inputs","title":"Optimized Multi-Location P-rep Design","text":"Import entries’ list? Choose whether import list entry numbers names genotypes treatments. selection , means app going generate synthetic data entries names treatment/genotypes based user inputs. selection Yes, entries list must fulfill specific format must .csv file. file must columns ENTRY NAME. ENTRY column must unique entry integer number treatment/genotype. column NAME must unique name identifies treatment/genotype. ENTRY NAME must unique, duplicates allowed. following table, show example entries list format. Enter number entries Input # Entries box comma separated list. example 150 entries, enter 150 box sample experiment. Select whether experiment contain checks Include checks? option. example experiment , set Yes. select Yes option, two boxes appear, first Input # Checks set many checks include experiment. case 3. Next option Input # Check’s Reps, set number replications check respectively comma separated list. replicating 3 checks 6 times, enter 6,6,6 box. Enter number locations Input # Locations. run experiment 5 locations, set Input # Locations 5. Set total number replications entries locations # Copies Per Entry dropdown box. example experiment, set 7. Select serpentine cartesian Plot Order Layout. example use default serpentine layout. ensure randomizations consistent across sessions, can set random seed box labeled Random Seed. example, set 2456. (Optional) Enter starting plot number Starting Plot Number box. Since experiment multiple locations, must enter comma separated list numbers length number locations input valid. example, set 1,1001,2001,3001,4001. (Optional) Enter location names Input Location Name box. Since experiment six locations, must enter comma separated list strings names environments. example, set LOC1,LOC2,LOC3,LOC4,LOC5. entered information experiment left side panel, click Run! button run design. prompted select dimensions field list options dropdown middle screen box labeled Select dimensions field. case, select 12 x 19. Click Randomize! button randomize experiment set field dimensions see output plots. change dimensions , must re-randomize. change inputs left side panel running experiment initially, click Run Randomize buttons , re-run new inputs.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/multi_location_prep.html","id":"outputs","dir":"Articles","previous_headings":"","what":"Outputs","title":"Optimized Multi-Location P-rep Design","text":"run Optimized Multi-Location P-rep Design FielDHub set dimensions field, several ways display information contained field book. first tab, Get Random, shows option change dimensions field re-randomize, well genotype allocation matrix generated optimized p-rep design, displays replications genotype location, much like matrix generated sparse allocation.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/multi_location_prep.html","id":"randomized-field","dir":"Articles","previous_headings":"Outputs","what":"Randomized Field","title":"Optimized Multi-Location P-rep Design","text":"Randomized Field tab displays graphical representation randomization entries field specified dimensions. replicated entries green colored cells, cells appearing location. display includes numbered labels rows columns. can copy field table save directly Excel file Copy Excel buttons top.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/multi_location_prep.html","id":"plot-number-field","dir":"Articles","previous_headings":"Outputs","what":"Plot Number Field","title":"Optimized Multi-Location P-rep Design","text":"Plot Number Field tab, table display field plots numbered according Plot Order Layout specified, either serpentine cartesian. can see corresponding entries plot number field book. Like Randomized Field tab, can copy table save Excel file Copy Excel buttons.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/multi_location_prep.html","id":"field-book","dir":"Articles","previous_headings":"Outputs","what":"Field Book","title":"Optimized Multi-Location P-rep Design","text":"Field Book displays information experimental design table format. contains specific plot number row column address entry, well corresponding treatment/genotype plot. table searchable, can filter data relevant columns.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/multi_location_prep.html","id":"using-the-fieldhub-function-multi_location_prep-","dir":"Articles","previous_headings":"","what":"2. Using the FielDHub function: multi_location_prep().","title":"Optimized Multi-Location P-rep Design","text":"can run design function multi_location_prep() FielDHub package. First, need load FielDHub package typing, , can enter information describing design like :","code":"library(FielDHub) optim_multi_prep <- multi_location_prep( lines = 150, l = 5, copies_per_entry = 7, checks = 3, rep_checks = c(6,6,6), plotNumber = c(1,1001,2001,3001,4001), locationNames = c(\"LOC1\", \"LOC2\", \"LOC3\", \"LOC4\", \"LOC5\"), seed = 2456 )"},{"path":"https://didiermurillof.github.io/FielDHub/articles/multi_location_prep.html","id":"details-on-the-inputs-entered-in-multi_location_prep-above","dir":"Articles","previous_headings":"2. Using the FielDHub function: multi_location_prep().","what":"Details on the inputs entered in multi_location_prep() above","title":"Optimized Multi-Location P-rep Design","text":"description inputs used generate design, lines = 150 number entries field. l = 5 number locations. copies_per_entry = 7 number copies entry. checks = 3 (optional) number checks. rep_checks = c(6,6,6) (optional) number replications check, vector length number checks. locationNames = c(\"LOC1\", \"LOC2\", \"LOC3\", \"LOC4\", \"LOC5\") optional names locations. seed = 2456 random seed replicate identical randomizations.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/multi_location_prep.html","id":"print-optim_multi_prep-object","dir":"Articles","previous_headings":"2. Using the FielDHub function: multi_location_prep().","what":"Print optim_multi_prep object","title":"Optimized Multi-Location P-rep Design","text":"print summary information object optim_multi_prep, can use generic function print(). multi_location_prep() function returns objects partially_replicated() addition list_locs, allocation, size_locations. object list_locs list data frames. data frame three columns; ENTRY, NAME REPS information randomize environment. object allocation binary allocation matrix genotypes locations, size_locations data frame column location row indicating size location (number field plots). example, can display allocation object. Let us print first ten genotypes allocation. Let us add two new columns allocation table. can add number copies genotype; 7 . can also add average allocation genotype. treatment appear 1.4 times average. can manipulate optim_multi_prep object list R. can first display design parameters randomizations following code: outputs:","code":"print(head(optim_multi_prep$allocation, 10)) LOC1 LOC2 LOC3 LOC4 LOC5 1 2 1 1 1 2 2 1 2 1 1 2 3 2 1 1 1 2 4 1 1 2 1 2 5 1 1 2 2 1 6 1 2 1 1 2 7 2 1 2 1 1 8 1 2 2 1 1 9 1 1 2 1 2 10 2 2 1 1 1 print(optim_multi_prep) Multi-Location Partially Replicated Design Replications within location: LOCATION Replicated Unreplicated 1 LOC1 63 90 2 LOC2 63 90 3 LOC3 63 90 4 LOC4 63 90 5 LOC5 63 90 Information on the design parameters: List of 7 $ rows : num [1:5] 19 19 19 19 19 $ columns : num [1:5] 12 12 12 12 12 $ min_distance : num [1:5] 3 3 3 3 3 $ incidence_in_rows: num [1:5] 4 2 3 5 2 $ locations : num 5 $ planter : chr \"serpentine\" $ seed : num 2456 10 First observations of the data frame with the partially_replicated field book: ID EXPT LOCATION YEAR PLOT ROW COLUMN CHECKS ENTRY TREATMENT 1 1 PrepExpt LOC1 2024 1 1 1 76 76 G-76 2 2 PrepExpt LOC1 2024 2 1 2 0 111 G-111 3 3 PrepExpt LOC1 2024 3 1 3 129 129 G-129 4 4 PrepExpt LOC1 2024 4 1 4 45 45 G-45 5 5 PrepExpt LOC1 2024 5 1 5 0 133 G-133 6 6 PrepExpt LOC1 2024 6 1 6 0 49 G-49 7 7 PrepExpt LOC1 2024 7 1 7 123 123 G-123 8 8 PrepExpt LOC1 2024 8 1 8 0 57 G-57 9 9 PrepExpt LOC1 2024 9 1 9 54 54 G-54 10 10 PrepExpt LOC1 2024 10 1 10 125 125 G-125"},{"path":"https://didiermurillof.github.io/FielDHub/articles/multi_location_prep.html","id":"access-to-optim_multi_prep-output","dir":"Articles","previous_headings":"2. Using the FielDHub function: multi_location_prep().","what":"Access to optim_multi_prep output","title":"Optimized Multi-Location P-rep Design","text":"objects accessible $ operator, .e.Β optim_multi_prep$layoutRandom[[1]] LOC1, optim_multi_prep$fieldBook fieldBook locations. optim_multi_prep$fieldBook data frame containing information every plot field, information location plot treatment plot. seen output , field book columns ID, EXPT, LOCATION, YEAR, PLOT, ROW, COLUMN, CHECKS, ENTRY, TREATMENT. Let us see first 10 rows field book experiment.","code":"field_book <- optim_multi_prep$fieldBook head(field_book, 10) ID EXPT LOCATION YEAR PLOT ROW COLUMN CHECKS ENTRY TREATMENT 1 1 PrepExpt LOC1 2024 1 1 1 76 76 G-76 2 2 PrepExpt LOC1 2024 2 1 2 0 111 G-111 3 3 PrepExpt LOC1 2024 3 1 3 129 129 G-129 4 4 PrepExpt LOC1 2024 4 1 4 45 45 G-45 5 5 PrepExpt LOC1 2024 5 1 5 0 133 G-133 6 6 PrepExpt LOC1 2024 6 1 6 0 49 G-49 7 7 PrepExpt LOC1 2024 7 1 7 123 123 G-123 8 8 PrepExpt LOC1 2024 8 1 8 0 57 G-57 9 9 PrepExpt LOC1 2024 9 1 9 54 54 G-54 10 10 PrepExpt LOC1 2024 10 1 10 125 125 G-125"},{"path":"https://didiermurillof.github.io/FielDHub/articles/multi_location_prep.html","id":"plot-field-layout","dir":"Articles","previous_headings":"2. Using the FielDHub function: multi_location_prep().","what":"Plot field layout","title":"Optimized Multi-Location P-rep Design","text":"plotting layout function coordinates ROW COLUMN field book object can use generic function plot() follows. plots first location, indexable location using dollar sign operator well.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/multi_location_prep.html","id":"field-layout-for-location-1","dir":"Articles","previous_headings":"2. Using the FielDHub function: multi_location_prep(). > Plot field layout","what":"Field Layout for Location 1","title":"Optimized Multi-Location P-rep Design","text":"figure , green plots contain replicated entries, gray plots contain entries appear .","code":"plot(optim_multi_prep, l = 1)"},{"path":"https://didiermurillof.github.io/FielDHub/articles/multi_location_prep.html","id":"field-layout-for-location-5","dir":"Articles","previous_headings":"2. Using the FielDHub function: multi_location_prep(). > Plot field layout","what":"Field Layout for Location 5","title":"Optimized Multi-Location P-rep Design","text":"Also, example location five:","code":"plot(optim_multi_prep, l = 5)"},{"path":[]},{"path":"https://didiermurillof.github.io/FielDHub/articles/news/fieldhub-0-1-0.html","id":"fieldhub-0-1-0","dir":"Articles > News","previous_headings":"","what":"FielDHub 0.1.0","title":"","text":"Photo Karsten WΓΌrth Unsplash","code":""},{"path":[]},{"path":[]},{"path":"https://didiermurillof.github.io/FielDHub/articles/news/fieldhub-0-1-0.html","id":"didier-murillo","dir":"Articles > News","previous_headings":"FielDHub 0.1.0","what":"Didier Murillo","title":"","text":"delighted announce initial release FielDHub CRAN! FielDHub conceived make quick easy generate, randomize, plot complex standard experimental designs. initial release version 0.1.0 recognition FielDHub development one year already used researchers NDSU, CIAT, well teaching. Install FielDHub :","code":"install.packages(\"FielDHub\")"},{"path":"https://didiermurillof.github.io/FielDHub/articles/news/fieldhub-0-1-0.html","id":"usage","dir":"Articles > News","previous_headings":"FielDHub 0.1.0","what":"Usage","title":"","text":"Get started using two simple lines code:","code":"library(FielDHub) run_app()"},{"path":"https://didiermurillof.github.io/FielDHub/articles/news/fieldhub-0-1-0.html","id":"relevant-features","dir":"Articles > News","previous_headings":"FielDHub 0.1.0","what":"Relevant Features","title":"","text":"Unreplicated partially replicated designs commonly used plant breeding forestry lack free tools available researchers make randomization. FielDHub provides easy way complete designs using app standalone functions diagonal_arrangement(), optimized_arrangement() RCBD_augmented(). Partially replicated design can done using function partially_replicated(). app provides novel features make randomization along field layout map. FielDHub’s features generating synthetic data along randomization, well plotting field layouts make app suitable teach statistic courses experimental design.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/news/fieldhub-0-1-0.html","id":"acknowledgements","dir":"Articles > News","previous_headings":"FielDHub 0.1.0","what":"Acknowledgements","title":"","text":"FielDHub long time coming, wouldn’t possible without devoted community users, many gone contribute fixes new ideas. like particularly thank Dr.Β Richard Horsley (Professor, Department Head & Barley Breeder Department Plant Sciences) sponsored development project. Also, big thanks go Dr.Β Ana MarΓ­a Heilman Dr.Β Andrew Green support plant breeding/biological background. project without contributions knowledge Dr.Β Salvador Gezan. came project critical moment ideas code, went beyond expected. Thank Johan Aparicio Thomas Walk contributions FielDHub. FielDHub submitted published Journal Open Source Software. peer review process done Thiago de Paula Oliveira (Reviewer), David LeBauer (Reviewer), Charlotte Soneson (Editor). Thank work, effort, contributions.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/news/fieldhub-1-2-0.html","id":"fieldhub-1-2-0","dir":"Articles > News","previous_headings":"","what":"FielDHub 1.2.0","title":"","text":"Photo Vackground Unsplash","code":""},{"path":[]},{"path":[]},{"path":"https://didiermurillof.github.io/FielDHub/articles/news/fieldhub-1-2-0.html","id":"didier-murillo","dir":"Articles > News","previous_headings":"FielDHub 1.2.0","what":"Didier Murillo","title":"","text":"happy announce release FielDHub v1.2.0. 12 months hard work commitment. new version comes many changes, new features, better graphical user interface design shiny app.","code":""},{"path":[]},{"path":"https://didiermurillof.github.io/FielDHub/articles/news/fieldhub-1-2-0.html","id":"shiny-app","dir":"Articles > News","previous_headings":"FielDHub 1.2.0","what":"Shiny App","title":"","text":"Added help menu option app connect directly documentation available GitHub repository. Added vignettes help documentation standard functions modules available designs app. Added capability making multiple randomizations across different locations unreplicated, partially replicated, lattice, RCBD, factorial, split-plot, split-split-plot, strip-plot, IBD, RCD designs. Added capability produce heatmap visualizations simulated data experimental designs. Added action buttons copy save field maps field book outputs Excel. Added factorization options aid users creation randomizations mapping layouts unreplicated partially replicated designs. Previous version required users * mathematical calculation priori. Added filters search boxes field book tables. Updated UI/UX design home page. Grouped single diagonal arrangement, multiple diagonal arrangement, optimized arrangement augmented RCB designs one single module. Added action run button experimental designs prevent reactivity issues application. Improved standardized user experience features readability access. Improved error logging messages. Added features inform end-users utilization correct input data file formats associated metadata/columns, checking duplicate values input files, well data type verification. Added additional field layout visualization/map options experimental designs. Previous version mapping options unreplicated p-rep designs. Added drop-menu display multiple layout mapping option shown entry number plot experimental designs. means, now can visualize randomization layout option locations input. Added option repeating whole entries/experiments unreplicated diagonal arrangement design multiple experiments (previously called decision blocks). Added check box feature Augmented RCB design allow creation nurseries option randomizing experimental entries . user decides leave option unchecked, checks randomized, experimental entries shown consecutive order. Added check box option RCB design allow continuous plot numbering independently rep block number. Previous version coded replication plot number (.e., 101 =rep1, 201=rep2, etc.). Fixed restriction RCBD mapping layout allow use 25 entries. PS: better designs number entries higher 25 (info go : FIELD PLOT DESIGN ).","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/news/fieldhub-1-2-0.html","id":"standalone-functions-in-fieldhub-package","dir":"Articles > News","previous_headings":"FielDHub 1.2.0","what":"Standalone Functions in FielDHub Package","title":"","text":"partially_replicated() now generates randomization across multiple locations/sites. diagonal_arrangement() now generates randomization across multiple locations/sites. optimized_arrangement() now generates randomization across multiple locations/sites. partially_replicated() now allows entries/treatments replicates. , required least unreplicated entries. Functions optimized_arrangement(), diagonal_arrangement() partially_replicated() now return feedback input dimensions nrows ncols incorrect. RCBD() now includes argument (continuous) manage way sets plotting number. RCBD_augmented() now allows customization field dimensions inputting number rows columns nrows ncols arguments. RCBD_augmented() now returns feedback input dimensions nrows ncols match data entered. RCBD_augmented() random = FALSE now allows randomizing checks/controls user wants. Fixed bug full_factorial() CRD factorial design prevented option including possible factorial combinations. Added method print() class fieldLayout. See print(). Added method plot() class FieldHub returns object class fieldLayout. See plot(). method plot() can plot field layout designs output. possible pass arguments location, layout order others. detail see plot(), print() summary() methods FielDHub. Fixed bug diagonal_arrangement() kindExpt = DBUDC. problem affected random distribution checks case unbalanced control plot numbers experiment. Fixed bug diagonal_arrangement() kindExpt = DBUDC. problem affected merging data user data randomization data users wanted replicated entries across experiments.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/news/fieldhub-1-2-0.html","id":"acknowledgements","dir":"Articles > News","previous_headings":"FielDHub 1.2.0","what":"Acknowledgements","title":"","text":"FielDHub v1.2.0 long time coming, wouldn’t possible without effort contribution Matthew Seefeldt. Thank Johan Aparicio bugs reported. Thank Ana MarΓ­a Heilman support leadership.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/news/fieldhub-1-3-1.html","id":"fieldhub-1-3-1","dir":"Articles > News","previous_headings":"","what":"FielDHub 1.3.1","title":"","text":"Photo Markus Spiske Pexels","code":""},{"path":[]},{"path":[]},{"path":"https://didiermurillof.github.io/FielDHub/articles/news/fieldhub-1-3-1.html","id":"didier-murillo","dir":"Articles > News","previous_headings":"FielDHub 1.3.1","what":"Didier Murillo","title":"","text":"thrilled announce release FielDHub v1.3.1, culmination dedicated effort hard work. updated version includes improvements new features, including sparse allocation, optimized multi-location p-rep, . excited share new capabilities users.","code":""},{"path":[]},{"path":"https://didiermurillof.github.io/FielDHub/articles/news/fieldhub-1-3-1.html","id":"new-features-in-the-shiny-app","dir":"Articles > News","previous_headings":"FielDHub 1.3.1","what":"New Features in the Shiny App","title":"","text":"Added module generate Sparse allocation. Added module generating Optimized Multi-Location Partially Replicated (p-rep). Added vignettes help documentation new modules; Sparse Allocations Optimized Multi-Location Partially Replicated (p-rep) Designs app.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/news/fieldhub-1-3-1.html","id":"enhancements","dir":"Articles > News","previous_headings":"FielDHub 1.3.1 > New Features in the Shiny App","what":"Enhancements:","title":"","text":"Renamed Partially Replicated module Single Multi-Location p-rep Improved usability field dimensions dropdown menu reordering options based absolute value difference number rows columns option. affects unreplicated partially replicated design modules.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/news/fieldhub-1-3-1.html","id":"fix-bugs","dir":"Articles > News","previous_headings":"FielDHub 1.3.1 > New Features in the Shiny App","what":"Fix bugs:","title":"","text":"Fixed issue: Upload data CRD module.","code":""},{"path":[]},{"path":"https://didiermurillof.github.io/FielDHub/articles/news/fieldhub-1-3-1.html","id":"standalone-functions","dir":"Articles > News","previous_headings":"FielDHub 1.3.1 > New Features in the FielDHub Package:","what":"Standalone Functions","title":"","text":"Created do_optim() function. function generates sparse p-rep allocation multiple locations. optimized allocation using incomplete blocks. Created sparse_allocation() function. new function uses function, do_optim(), generate sparse allocation, uses function diagonal_arrangement() create unreplicated designs across multiple locations. Created multi_location_prep() function. uses within optimization function do_optim() generate partially replicated (p-rep) allocation, uses function partially_replicated() create p-rep designs across multiple locations. Created pairs_distance() function. function calculates pairwise distances elements matrix appears twice . Created swap_pairs() function. swaps pairs matrix integers optimizes p-rep design. function modifies input matrix XX ensure distance two occurrences integer least distance dd, swapping one occurrences random occurrence different integer least dd away. function starts starting dist d=3d = 3 increases 11 algorithm longer converges max number iterations performed. Created search_matrix_values() function. looks values appear row matrix return row number, value, frequency. Added optimization process partially replicated (p-rep) designs. uses function swap_pairs(). Added vignettes help documentation new functions.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/news/fieldhub-1-3-1.html","id":"enhancements-1","dir":"Articles > News","previous_headings":"FielDHub 1.3.1 > New Features in the FielDHub Package:","what":"Enhancements:","title":"","text":"partially_replicated() accepts custom field dimensions location. example, nrows = c(23, 20, 20) ncols = c(20, 23, 23) field rows columns three environments. Code refactoring diagonal_arrangement() function. Code refactoring utility function pREP(). Avoid cyclic reps incomplete block designs number treatments square.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/news/fieldhub-1-3-1.html","id":"acknowledgements","dir":"Articles > News","previous_headings":"","what":"Acknowledgements","title":"","text":"FielDHub v1.3.1 results dedicated effort contribution group individuals made release possible. want extend sincere gratitude Mr.Β Jean-Marc Montpetit contributions developing swap_pairs() pairs_distance() functions. help enhanced optimization partially replicated (p-rep) design. Thank , Dr.Β Salvador Gezan, contributions fresh ideas. also thank Matthew Seefeldt helping write documentation Johan Aparicio ideas reporting bugs. Thanks, Ana MarΓ­a Heilman, support leadership throughout development process.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/optimized_arrangement.html","id":"overview","dir":"Articles","previous_headings":"","what":"Overview","title":"Un-replicated Optimized Arrangement Design","text":"One un-replicated design can use FielDHub optimized arrangement. Unlike diagonal design, optimized arrangement completely randomizes positions checks instead putting systematic diagonal pattern(Clarke Stefanova 2011). Randomization subject restrictions. restrictions seek optimize distribution control plots field ensure spread keeping minimum distance . FielDHub includes function run experimental designs, features include options set number entries number checks experiment. Users can also choose run experiment multiple locations.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/optimized_arrangement.html","id":"use-case","dir":"Articles","previous_headings":"","what":"Use case","title":"Un-replicated Optimized Arrangement Design","text":"early generation plant breeding project needs test 401 genotypes winter wheat. planned carry experiment field containing 29 rows 15 columns plots. project, 401 genotypes allocated one experiment tested three locations. addition, three checks randomly included across field fill 34 plots representing 7.8% total number experimental plots.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/optimized_arrangement.html","id":"running-the-shiny-app","dir":"Articles","previous_headings":"","what":"Running the Shiny App","title":"Un-replicated Optimized Arrangement Design","text":"launch app need run either, ","code":"FielDHub::run_app() library(FielDHub) run_app()"},{"path":"https://didiermurillof.github.io/FielDHub/articles/optimized_arrangement.html","id":"using-the-fieldhub-shiny-app","dir":"Articles","previous_headings":"","what":"1. Using the FielDHub Shiny App","title":"Un-replicated Optimized Arrangement Design","text":"app running, go un-replicated Designs > Optimized Arrangement , follow following steps show generate un-replicated optimized arrangement design.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/optimized_arrangement.html","id":"inputs","dir":"Articles","previous_headings":"","what":"Inputs","title":"Un-replicated Optimized Arrangement Design","text":"selection , means app going generate synthetic data entries names treatment/genotypes based user inputs. selection Yes, entries list must fulfill specific format must .csv file. file must columns ENTRY, NAME, REPS. ENTRY column must unique entry integer number treatment/genotype. column NAME must unique name identifies treatment/genotype. REPS column must integer entry replications checks entries. ENTRY NAME must unique, duplicates allowed. following table, show example entries list format. example entry list three checks nine treatments/genotypes. crucial allocate checks top part file. Enter number checks Input # Checks box, 3 case. Enter number replications checks comma separated list containing number check Input # Check’s Reps box. example experiment, enter 12,11,11. Enter number entries/treatments Input # Entries box, 401 case. Select serpentine cartesian Plot Order Layout. example set serpentine layout. Since want run experiment 3 locations, set Input # Locations 3. ensure randomizations consistent across sessions, can set random seed box labeled random seed. instance, set 130. Enter name experiment Input Experiment Name box. example, PYT_WHEAT_22. Enter starting plot number Starting Plot Number box. experiment multiple locations, must enter comma separated list numbers length number locations input valid. Since 3 locations experiment, enter 1001,2001,3001. Enter name site/location Input Location box. case run experiment three locations, name location must enter separate comma, example: FARGO, CASSELTON, MINOT. entered information experiment left side panel, click Run! button run design. prompted select dimensions field list options drop middle screen box labeled Select dimensions field. case, select 15 x 29. Click Randomize! button randomize experiment set field dimensions see output plots. change dimensions , must re-randomize. change inputs left side panel running experiment initially, click Run Randomize buttons , re-run new inputs.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/optimized_arrangement.html","id":"outputs","dir":"Articles","previous_headings":"","what":"Outputs","title":"Un-replicated Optimized Arrangement Design","text":"run un-replicated optimized arrangement design FielDHub set dimensions field, several ways display information contained field book. first tab, Get Random, shows option change dimensions field re-randomize, well reference guide experiment design.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/optimized_arrangement.html","id":"data-input","dir":"Articles","previous_headings":"Outputs","what":"Data Input","title":"Un-replicated Optimized Arrangement Design","text":"second tab, Data Input, can see entries randomization list, well table checks number times appear field. list entries, reps check included well.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/optimized_arrangement.html","id":"randomized-field","dir":"Articles","previous_headings":"Outputs","what":"Randomized Field","title":"Un-replicated Optimized Arrangement Design","text":"Randomized Field tab displays graphical representation randomization entries field specified dimensions. checks colored uniquely, showing number times distributed throughout field. display includes numbered labels rows columns. can copy field table save directly Excel file Copy Excel buttons top.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/optimized_arrangement.html","id":"plot-number-field","dir":"Articles","previous_headings":"Outputs","what":"Plot Number Field","title":"Un-replicated Optimized Arrangement Design","text":"Plot Number Field tab, table display field plots numbered according Plot Order Layout specified, either serpentine cartesian. can see corresponding entries plot number field book. Like Randomized Field tab, can copy table save Excel file Copy Excel buttons.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/optimized_arrangement.html","id":"field-book","dir":"Articles","previous_headings":"Outputs","what":"Field Book","title":"Un-replicated Optimized Arrangement Design","text":"Field Book displays information experimental design table format. contains specific plot number row column address entry, well corresponding treatment plot. table searchable, can filter data relevant columns.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/optimized_arrangement.html","id":"using-the-fieldhub-function-optimized_arrangement-","dir":"Articles","previous_headings":"","what":"2. Using the FielDHub function: optimized_arrangement().","title":"Un-replicated Optimized Arrangement Design","text":"can run design function optimized_arrangement() FielDHub package. First, need load FielDHub package typing, , can enter information describing design like :","code":"library(FielDHub) optim_expt <- optimized_arrangement( nrows = 29, ncols = 15, lines = 401, amountChecks = c(12,11,11), checks = 3, l = 3, plotNumber = c(1001,2001,3001), exptName = \"WINTER_WHEAT_22\", locationNames = c(\"FARGO\", \"CASSELTON\", \"MINOT\"), seed = 130 )"},{"path":"https://didiermurillof.github.io/FielDHub/articles/optimized_arrangement.html","id":"details-on-the-inputs-entered-in-optimized_arrangement-above","dir":"Articles","previous_headings":"2. Using the FielDHub function: optimized_arrangement().","what":"Details on the inputs entered in optimized_arrangement() above","title":"Un-replicated Optimized Arrangement Design","text":"description inputs used generate design, nrows = 29 number rows field. ncols = 15 number columns field. lines = 401 number entries amountChecks = c(12,11,11) values representing respective replicates check, integer total number checks. checks = 3 number checks. l = 3 number locations. plotNumber = c(1001,2001,3001) starting plot number location respectively, single number 1 location. exptName = \"WINTER_WHEAT_22\" optional name experiment. locationNames = c(\"FARGO\", \"CASSELTON\", \"MINOT\") values representing respective name location. seed = 130 random seed replicate identical randomizations.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/optimized_arrangement.html","id":"print-optim_expt-object","dir":"Articles","previous_headings":"2. Using the FielDHub function: optimized_arrangement().","what":"Print optim_expt object","title":"Un-replicated Optimized Arrangement Design","text":"print summary information object optim_expt, can use generic function print().","code":"print(optim_expt) Un-replicated Optimized Arrangement Design Information on the design parameters: List of 10 $ rows : num 29 $ columns : num 15 $ min_distance: num [1:3] 2 3.16 2.24 $ treatments : num 401 $ checks : int 3 $ entry_checks: int [1:3] 1 2 3 $ rep_checks : num [1:3] 12 11 11 $ locations : num 3 $ planter : chr \"serpentine\" $ seed : num 130 10 First observations of the data frame with the optimized_arrangement field book: ID EXPT LOCATION YEAR PLOT ROW COLUMN CHECKS ENTRY TREATMENT 1 1 WINTER_WHEAT_22 FARGO 2024 1001 1 1 0 70 G70 2 2 WINTER_WHEAT_22 FARGO 2024 1002 1 2 0 357 G357 3 3 WINTER_WHEAT_22 FARGO 2024 1003 1 3 0 217 G217 4 4 WINTER_WHEAT_22 FARGO 2024 1004 1 4 0 280 G280 5 5 WINTER_WHEAT_22 FARGO 2024 1005 1 5 0 259 G259 6 6 WINTER_WHEAT_22 FARGO 2024 1006 1 6 0 50 G50 7 7 WINTER_WHEAT_22 FARGO 2024 1007 1 7 0 223 G223 8 8 WINTER_WHEAT_22 FARGO 2024 1008 1 8 0 348 G348 9 9 WINTER_WHEAT_22 FARGO 2024 1009 1 9 0 180 G180 10 10 WINTER_WHEAT_22 FARGO 2024 1010 1 10 0 153 G153"},{"path":"https://didiermurillof.github.io/FielDHub/articles/optimized_arrangement.html","id":"access-to-optim_expt-object","dir":"Articles","previous_headings":"2. Using the FielDHub function: optimized_arrangement().","what":"Access to optim_expt object","title":"Un-replicated Optimized Arrangement Design","text":"optimized_arrangement() function returns list consisting information displayed output tabs FielDHub app: design information, plot layout, plot numbering, entries list, field book. Accessible $ operator, .e.Β optim_expt$layoutRandom optim_expt$fieldBook. optim_expt$fieldBook data frame containing information every plot field, information location plot treatment plot. seen output , field book columns ID, EXPT, LOCATION, YEAR, PLOT, ROW, COLUMN, CHECKS, ENTRY, TREATMENT. Let us see first 10 rows field book experiment.","code":"field_book <- optim_expt$fieldBook head(field_book, 10) ID EXPT LOCATION YEAR PLOT ROW COLUMN CHECKS ENTRY TREATMENT 1 1 WINTER_WHEAT_22 FARGO 2024 1001 1 1 0 70 G70 2 2 WINTER_WHEAT_22 FARGO 2024 1002 1 2 0 357 G357 3 3 WINTER_WHEAT_22 FARGO 2024 1003 1 3 0 217 G217 4 4 WINTER_WHEAT_22 FARGO 2024 1004 1 4 0 280 G280 5 5 WINTER_WHEAT_22 FARGO 2024 1005 1 5 0 259 G259 6 6 WINTER_WHEAT_22 FARGO 2024 1006 1 6 0 50 G50 7 7 WINTER_WHEAT_22 FARGO 2024 1007 1 7 0 223 G223 8 8 WINTER_WHEAT_22 FARGO 2024 1008 1 8 0 348 G348 9 9 WINTER_WHEAT_22 FARGO 2024 1009 1 9 0 180 G180 10 10 WINTER_WHEAT_22 FARGO 2024 1010 1 10 0 153 G153"},{"path":"https://didiermurillof.github.io/FielDHub/articles/optimized_arrangement.html","id":"plot-the-field-layout","dir":"Articles","previous_headings":"2. Using the FielDHub function: optimized_arrangement().","what":"Plot the field layout","title":"Un-replicated Optimized Arrangement Design","text":"plotting layout function coordinates ROW COLUMN field book object can use generic function plot() follow, figure shows map experiment randomized un-replicated optimized arrangement design. Gray plots represent un-replicated treatments, distinctively colored check plots randomly replicated throughout field. possible pass arguments plot() specific location. example, can plot specifically layout location 2.","code":"plot(optim_expt) plot(optim_expt, l = 2)"},{"path":[]},{"path":"https://didiermurillof.github.io/FielDHub/articles/partially_replicated.html","id":"overview","dir":"Articles","previous_headings":"","what":"Overview","title":"Partially Replicated (p-rep) Design","text":"Partially replicated designs commonly employed early generation field trials. type design characterized replication portion entries, remaining entries appearing experiment. Commonly, part treatments reps due arbitrary decision research, also cases, due technical reasons. replication ratio typically 1:4 (Cullis 2006), means portion treatment repeated twice p = 25%. However, design can adapted meet specific needs adjusting values pp level replication. example, standard varieties (checks) may included higher levels replication test lines. FielDHub, users can set number entries reps, well number entries appear . can also choose run experiment multiple locations.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/partially_replicated.html","id":"optimization","dir":"Articles","previous_headings":"","what":"Optimization","title":"Partially Replicated (p-rep) Design","text":"partially replicated (p-rep) design location undergoes optimization process involves following procedure: Given matrix XX integers (p-rep design within location), want ensure distance two occurrences treatment least distance dd. specifically, want modify XX ensure treatments appear twice within distance less dd resulting matrix. goal optimization process find modified matrix satisfies constraint maximizing measure deviation original matrix XX. case, measure deviation pairwise Euclidean distance occurrences treatment. process done function swap_pairs() uses heuristic algorithm starts distance d=3d = 3 pairs occurrences treatment, increases distance 11 repeats process either algorithm longer converges maximum number iterations reached. algorithm works first identifying pairs occurrences treatment closer dd. pair, function selects random occurrence different integer least dd away, swaps two occurrences. process repeated swaps can made increase pairwise Euclidean distances occurrences treatment.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/partially_replicated.html","id":"toy-example","dir":"Articles","previous_headings":"Optimization","what":"Toy Example","title":"Partially Replicated (p-rep) Design","text":"Consider p-rep design ten treatments replicated twice forty . matrix (field layout) experiment 6 rows 10 columns. X=X = initial p-rep design, notice two instances treatment 5 positioned next . Additionally, treatments 7 9 also situated adjacent cells. suboptimal allocations lead issues inaccurate results analyzing data experiment due short distance replicated treatments likely spatial correlation . following table shows pairwise distances replicated treatments","code":"[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [1,] 21 40 17 25 26 3 11 31 36 6 [2,] 5 5 33 8 48 29 43 23 1 45 [3,] 41 27 38 39 7 28 14 22 24 4 [4,] 4 47 18 7 2 35 6 20 12 46 [5,] 3 15 9 34 49 50 2 10 42 8 [6,] 32 16 19 9 10 13 37 1 44 30 geno Pos1 Pos2 DIST rA cA rB cB 5 5 2 8 1.000000 2 1 2 2 7 7 22 27 1.414214 4 4 3 5 9 9 17 24 1.414214 5 3 6 4 2 2 28 41 2.236068 4 5 5 7 10 10 30 47 3.162278 6 5 5 8 1 1 48 50 4.123106 6 8 2 9 6 6 40 55 4.242641 4 7 1 10 3 3 5 31 6.403124 5 1 1 6 8 8 20 59 6.708204 2 4 5 10 4 4 4 57 9.055385 4 1 3 10"},{"path":"https://didiermurillof.github.io/FielDHub/articles/partially_replicated.html","id":"swap-pairs","dir":"Articles","previous_headings":"Optimization","what":"Swap pairs","title":"Partially Replicated (p-rep) Design","text":"can improve efficiency design swapping treatments close next using function swap_pairs() FielDHub R package. new matrix optimized p-rep design , distances pairwise treatments , can see, minimum distance algorithm reached 5. means treatments appear twice within distance less 5 resulting prep design. considerable improvement first version p-rep design FielDHub function partially_replicated() internally optimization process uses function swap_pairs() maximize distance replicated treatments.","code":"library(FielDHub) B <- swap_pairs(X, starting_dist = 3) print(B$optim_design) [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [1,] 8 35 6 2 33 44 3 4 37 6 [2,] 43 30 25 5 39 29 19 11 36 45 [3,] 40 13 38 10 20 28 15 41 10 17 [4,] 1 27 18 31 32 22 24 21 12 5 [5,] 23 47 3 34 49 50 16 46 14 48 [6,] 7 26 2 42 9 1 8 7 4 9 print(B$pairwise_distance) geno Pos1 Pos2 DIST rA cA rB cB 9 9 30 60 5.000000 6 5 6 10 10 10 21 51 5.000000 3 4 3 9 2 2 18 19 5.099020 6 3 1 4 4 4 43 54 5.099020 1 8 6 9 1 1 4 36 5.385165 4 1 6 6 3 3 17 37 5.656854 5 3 1 7 5 5 20 58 6.324555 2 4 4 10 6 6 13 55 7.000000 1 3 1 10 7 7 6 48 7.000000 6 1 6 8 8 8 1 42 7.810250 1 1 6 7"},{"path":"https://didiermurillof.github.io/FielDHub/articles/partially_replicated.html","id":"acknowledge","dir":"Articles","previous_headings":"Optimization","what":"Acknowledge","title":"Partially Replicated (p-rep) Design","text":"like acknowledge Mr.Β Jean-Marc Montpetit contributing code ideas swap_pairs() pairs_distance() functions. contributions significant impact improving partially replicated (p-rep) design R package FielDHub. thank valuable contributions.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/partially_replicated.html","id":"use-case","dir":"Articles","previous_headings":"","what":"Use case","title":"Partially Replicated (p-rep) Design","text":"Consider plant breeding field trial 300 plots containing 75 entries appearing two times , 150 entries appearing . field trial arranged field 15 rows 20 columns. case, breeder decided replicate genotypes share significant generic information (75), well leave just one copy genotypes siblings (150).","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/partially_replicated.html","id":"running-the-shiny-app","dir":"Articles","previous_headings":"","what":"Running the Shiny App","title":"Partially Replicated (p-rep) Design","text":"launch app need run either ","code":"FielDHub::run_app() library(FielDHub) run_app()"},{"path":"https://didiermurillof.github.io/FielDHub/articles/partially_replicated.html","id":"using-the-fieldhub-shiny-app","dir":"Articles","previous_headings":"","what":"1. Using the FielDHub Shiny App","title":"Partially Replicated (p-rep) Design","text":"app running, click tab Partially Replicated Design select Single Multi-Location p-rep dropdown. , follow following steps show generate partially replicated design.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/partially_replicated.html","id":"inputs","dir":"Articles","previous_headings":"","what":"Inputs","title":"Partially Replicated (p-rep) Design","text":"selection , means app going generate synthetic data entries names treatment/genotypes based user inputs. selection Yes, entries list must fulfill specific format must .csv file. file must columns ENTRY, NAME, REPS. ENTRY column must unique entry integer number treatment/genotype. column NAME must unique name identifies treatment/genotype. REPS column must integer number replications groups. ENTRY NAME must unique, duplicates allowed. following table, show example entries list format. example entry list four treatments/genotypes appear twice 8 appear just . Enter number entries per replicate group # Entries per Rep Group box comma separated list. example 2 groups 85 130 entries. , enter 75, 150 box sample experiment. Enter number replications per group # Rep per Group box. example 2 1 replications 2 groups, enter 2, 1 box. Enter number locations Input # Locations. run experiment single location, set Input # Locations 1. Select serpentine cartesian Plot Order Layout. example use default serpentine layout. ensure randomizations consistent across sessions, can set random seed box labeled random seed. example, set 1245. Enter starting plot number Starting Plot Number box. experiment multiple locations, must enter comma separated list numbers length number locations input valid. entered information experiment left side panel, click Run! button run design. prompted select dimensions field list options drop middle screen box labeled Select dimensions field. case, select 15 x 20. Click Randomize! button randomize experiment set field dimensions see output plots. change dimensions , must re-randomize. change inputs left side panel running experiment initially, click Run Randomize buttons , re-run new inputs.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/partially_replicated.html","id":"outputs","dir":"Articles","previous_headings":"","what":"Outputs","title":"Partially Replicated (p-rep) Design","text":"run single diagonal arrangement FielDHub set dimensions field, several ways display information contained field book. first tab, Get Random, shows option change dimensions field re-randomize, well reference guide experiment design.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/partially_replicated.html","id":"data-input","dir":"Articles","previous_headings":"Outputs","what":"Data Input","title":"Partially Replicated (p-rep) Design","text":"second tab, Data Input, can see entries randomization list, well table checks number times appear field. list entries, reps check included well.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/partially_replicated.html","id":"randomized-field","dir":"Articles","previous_headings":"Outputs","what":"Randomized Field","title":"Partially Replicated (p-rep) Design","text":"Randomized Field tab displays graphical representation randomization entries field specified dimensions. checks green colored cells, display includes numbered labels rows columns. can copy field table save directly Excel file Copy Excel buttons top.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/partially_replicated.html","id":"plot-number-field","dir":"Articles","previous_headings":"Outputs","what":"Plot Number Field","title":"Partially Replicated (p-rep) Design","text":"Plot Number Field tab, table display field plots numbered according Plot Order Layout specified, either serpentine cartesian. can see corresponding entries plot number field book. Like Randomized Field tab, can copy table save Excel file Copy Excel buttons.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/partially_replicated.html","id":"field-book","dir":"Articles","previous_headings":"Outputs","what":"Field Book","title":"Partially Replicated (p-rep) Design","text":"Field Book displays information experimental design table format. contains specific plot number row column address entry, well corresponding treatment plot. table searchable, can filter data relevant columns.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/partially_replicated.html","id":"using-the-fieldhub-function-partially_replicated-","dir":"Articles","previous_headings":"","what":"2. Using the FielDHub function: partially_replicated().","title":"Partially Replicated (p-rep) Design","text":"can run design function partially_replicated() FielDHub package. First, need load FielDHub package typing, , can enter information describing design like :","code":"library(FielDHub) prep <- partially_replicated( nrows = 15, ncols = 20, repGens = c(75,150), repUnits = c(2,1), planter = \"serpentine\", plotNumber = 101, l = 1, exptName = \"Expt1\", locationNames = \"PALMIRA\", seed = 1245, )"},{"path":"https://didiermurillof.github.io/FielDHub/articles/partially_replicated.html","id":"details-on-the-inputs-entered-in-optimized_arrangement-above","dir":"Articles","previous_headings":"2. Using the FielDHub function: partially_replicated().","what":"Details on the inputs entered in optimized_arrangement() above","title":"Partially Replicated (p-rep) Design","text":"description inputs used generate design, nrows = 15 number rows field. ncols = 20 number columns field. repGens = c(75,150) values groups replicate repUnits = c(2,1) values representing respective replicates group. planter = \"serpentine\" layout order. plotNumber = 101 starting plot number experiment. l = 1 number locations. exptName = \"Expt1\" optional name experiment. locationNames = \"PALMIRA\" optional name locations. seed = 1245 random seed replicate identical randomizations.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/partially_replicated.html","id":"print-prep-object","dir":"Articles","previous_headings":"2. Using the FielDHub function: partially_replicated().","what":"Print prep object","title":"Partially Replicated (p-rep) Design","text":"print summary information object prep, can use generic function print().","code":"print(prep) Partially Replicated Design Replications within location: LOCATION Replicated Unreplicated 1 PALMIRA 75 150 Information on the design parameters: List of 7 $ rows : num 15 $ columns : num 20 $ min_distance : num 7 $ incidence_in_rows: num 3 $ locations : num 1 $ planter : chr \"serpentine\" $ seed : num 1245 10 First observations of the data frame with the partially_replicated field book: ID EXPT LOCATION YEAR PLOT ROW COLUMN CHECKS ENTRY TREATMENT 1 1 Expt1 PALMIRA 2024 101 1 1 44 44 G44 2 2 Expt1 PALMIRA 2024 102 1 2 0 102 G102 3 3 Expt1 PALMIRA 2024 103 1 3 71 71 G71 4 4 Expt1 PALMIRA 2024 104 1 4 0 107 G107 5 5 Expt1 PALMIRA 2024 105 1 5 8 8 G8 6 6 Expt1 PALMIRA 2024 106 1 6 13 13 G13 7 7 Expt1 PALMIRA 2024 107 1 7 0 170 G170 8 8 Expt1 PALMIRA 2024 108 1 8 67 67 G67 9 9 Expt1 PALMIRA 2024 109 1 9 0 123 G123 10 10 Expt1 PALMIRA 2024 110 1 10 0 105 G105"},{"path":"https://didiermurillof.github.io/FielDHub/articles/partially_replicated.html","id":"access-to-prep-output","dir":"Articles","previous_headings":"2. Using the FielDHub function: partially_replicated().","what":"Access to prep output","title":"Partially Replicated (p-rep) Design","text":"partially_replicated() function returns list consisting information displayed output tabs FielDHub app: design information, plot layout, plot numbering, entries list, field book. Accessible $ operator, .e.Β prep$layoutRandom prep$fieldBook. prep$fieldBook list containing information every plot field, information location plot treatment plot. seen output , field book columns ID, EXPT, LOCATION, YEAR, PLOT, ROW, COLUMN, CHECKS, ENTRY, TREATMENT. Let us see first 10 rows field book experiment.","code":"field_book <- prep$fieldBook head(field_book, 10) ID EXPT LOCATION YEAR PLOT ROW COLUMN CHECKS ENTRY TREATMENT 1 1 Expt1 PALMIRA 2024 101 1 1 44 44 G44 2 2 Expt1 PALMIRA 2024 102 1 2 0 102 G102 3 3 Expt1 PALMIRA 2024 103 1 3 71 71 G71 4 4 Expt1 PALMIRA 2024 104 1 4 0 107 G107 5 5 Expt1 PALMIRA 2024 105 1 5 8 8 G8 6 6 Expt1 PALMIRA 2024 106 1 6 13 13 G13 7 7 Expt1 PALMIRA 2024 107 1 7 0 170 G170 8 8 Expt1 PALMIRA 2024 108 1 8 67 67 G67 9 9 Expt1 PALMIRA 2024 109 1 9 0 123 G123 10 10 Expt1 PALMIRA 2024 110 1 10 0 105 G105"},{"path":"https://didiermurillof.github.io/FielDHub/articles/partially_replicated.html","id":"plot-field-layout","dir":"Articles","previous_headings":"2. Using the FielDHub function: partially_replicated().","what":"Plot field layout","title":"Partially Replicated (p-rep) Design","text":"plotting layout function coordinates ROW COLUMN field book object can use generic function plot() follow, figure , green plots contain replicated entries, gray plots contain entries appear .","code":"plot(prep)"},{"path":[]},{"path":"https://didiermurillof.github.io/FielDHub/articles/rcbd.html","id":"using-the-fieldhub-shiny-app","dir":"Articles","previous_headings":"","what":"1. Using the FielDHub Shiny App","title":"Randomized Complete Block Design","text":"launch app need run either app running, go Designs > Randomized Complete Block Designs (RCBD) , follow following steps show generate kind design example 24 treatments 4 reps. run experiment just one location.","code":"FielDHub::run_app() library(FielDHub) run_app()"},{"path":"https://didiermurillof.github.io/FielDHub/articles/rcbd.html","id":"inputs","dir":"Articles","previous_headings":"","what":"Inputs","title":"Randomized Complete Block Design","text":"selection , means app going generate synthetic data entries names treatment/genotypes based user inputs. selection Yes, entries list must fulfill specific format must .csv file. file must single column TREATMENT, containing list unique names identify treatment/genotype. Duplicate values allowed, entries must unique. following, show example entries list format. example entry list 10 treatments. Input number treatments Input # Treatments box. Set 24. Select number replications treatments Input # Full Reps box. number treatments number full reps set dimensions field. Set 4. Enter number locations Input # Locations. run experiment single location, set 1. Select serpentine cartesian Plot Order Layout. example use default serpentine layout. Enter starting plot number Starting Plot Number box. experiment multiple locations, must enter comma separated list numbers length number locations input valid. case, set 101. Enter name location experiment Input Location box. multiple locations, name must comma separated list. Set \"FARGO\". ensure randomizations consistent across sessions, can set random seed box labeled random seed. example, set 1237. entered information experiment left side panel, click Run! button run design.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/rcbd.html","id":"outputs","dir":"Articles","previous_headings":"","what":"Outputs","title":"Randomized Complete Block Design","text":"run randomized complete block design FielDHub, several ways display information contained field book.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/rcbd.html","id":"field-layout","dir":"Articles","previous_headings":"Outputs","what":"Field Layout","title":"Randomized Complete Block Design","text":"first click run button randomized complete block design, FielDHub displays Field Layout tab, shows entries arrangement field. box display, can change layout field change location displayed. can also display heatmap field changing Type Plot Heatmap. view heatmap, must first simulate experiment described field Simulate! button. pop-window appear can enter variable want simulate along minimum maximum values.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/rcbd.html","id":"field-book","dir":"Articles","previous_headings":"Outputs","what":"Field Book","title":"Randomized Complete Block Design","text":"Field Book displays information experimental design table format. contains specific plot number row column address entry, well corresponding treatment plot. table searchable, can filter data relevant columns. simulated data heatmap, additional column variable appears field book.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/rcbd.html","id":"using-the-fieldhub-function-rcbd","dir":"Articles","previous_headings":"","what":"2. Using the FielDHub function: RCBD()","title":"Randomized Complete Block Design","text":"can run design function FielDHub package, RCBD(). First, need load FielDHub package typing, , can enter information describing design like :","code":"library(FielDHub) rcbd <- RCBD( t = 24, reps = 4, l = 1, plotNumber = 101, locationNames = \"FARGO\", seed = 1237 )"},{"path":"https://didiermurillof.github.io/FielDHub/articles/rcbd.html","id":"details-on-the-inputs-entered-in-rcbd-above","dir":"Articles","previous_headings":"2. Using the FielDHub function: RCBD()","what":"Details on the inputs entered in RCBD() above","title":"Randomized Complete Block Design","text":"description inputs used generate design, t = 24 number treatments. reps = 4 number replications treatment. l = 1 number locations. plotNumber = 101 starting plot number. locationNames = \"FARGO\" optional name location. seed = 1234 random seed replicate identical randomizations.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/rcbd.html","id":"print-rcbd-object","dir":"Articles","previous_headings":"2. Using the FielDHub function: RCBD()","what":"Print rcbd object","title":"Randomized Complete Block Design","text":"","code":"print(rcbd) Randomized Complete Block Design (RCBD): Information on the design parameters: List of 7 $ blocks : num 4 $ number.of.treatments: num 24 $ treatments : chr [1:24] \"T1\" \"T2\" \"T3\" \"T4\" ... $ locations : num 1 $ plotNumber : num [1:4] 101 201 301 401 $ locationNames : chr \"FARGO\" $ seed : num 1237 10 First observations of the data frame with the RCBD field book: ID LOCATION PLOT REP TREATMENT 1 1 FARGO 101 1 T6 2 2 FARGO 102 1 T1 3 3 FARGO 103 1 T21 4 4 FARGO 104 1 T7 5 5 FARGO 105 1 T14 6 6 FARGO 106 1 T17 7 7 FARGO 107 1 T12 8 8 FARGO 108 1 T11 9 9 FARGO 109 1 T3 10 10 FARGO 110 1 T5"},{"path":"https://didiermurillof.github.io/FielDHub/articles/rcbd.html","id":"access-to-rcbd-object","dir":"Articles","previous_headings":"2. Using the FielDHub function: RCBD()","what":"Access to RCBD object","title":"Randomized Complete Block Design","text":"function RCBD returns list consisting information displayed output tabs FielDHub app: design information, plot layout, plot numbering, entries list, field book. accessible $ operator, .e.Β rcbd$layoutRandom rcbd$fieldBook. rcbd$fieldBook list containing information every plot field, information location plot treatment plot. seen output , field book columns ID, LOCATION, PLOT, REP, IBLOCK, UNIT, ENTRY, TREATMENT.","code":"field_book <- rcbd$fieldBook head(rcbd$fieldBook, 10) ID LOCATION PLOT REP TREATMENT 1 1 FARGO 101 1 T6 2 2 FARGO 102 1 T1 3 3 FARGO 103 1 T21 4 4 FARGO 104 1 T7 5 5 FARGO 105 1 T14 6 6 FARGO 106 1 T17 7 7 FARGO 107 1 T12 8 8 FARGO 108 1 T11 9 9 FARGO 109 1 T3 10 10 FARGO 110 1 T5"},{"path":"https://didiermurillof.github.io/FielDHub/articles/rcbd.html","id":"plot-the-field-layout","dir":"Articles","previous_headings":"2. Using the FielDHub function: RCBD()","what":"Plot the field layout","title":"Randomized Complete Block Design","text":"plotting layout function coordinates ROW COLUMN, can use generic function plot() follows,","code":"plot(rcbd)"},{"path":"https://didiermurillof.github.io/FielDHub/articles/rectangular_lattice.html","id":"using-the-fieldhub-shiny-app","dir":"Articles","previous_headings":"","what":"1. Using the FielDHub Shiny App","title":"Rectangular Lattice Design","text":"generate rectangular lattice design using FielDHub app: First, go Lattice Designs > Rectangular Lattice , follow following steps show generate rectangular lattice design 56 treatments 3 reps.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/rectangular_lattice.html","id":"inputs","dir":"Articles","previous_headings":"","what":"Inputs","title":"Rectangular Lattice Design","text":"selection , means app going generate synthetic data entries names treatment based user inputs. selection Yes, entries list must fulfill specific format must .csv file. file must columns ENTRY NAME. ENTRY column must unique entry integer number treatment. column NAME must unique name identifies treatment. ENTRY NAME must unique, duplicates allowed. following table, show example entries list format. example entry list 12 treatments. Input number treatments Input # Treatments box. rectangular lattice design, number treatments must rectangular number, product two consecutive integers. example, 7 x 8 = 56 valid entry, use example. Select number replications treatments Input # Full Reps box, 3. Set number plots incomplete block Input # Plots per IBlock box, 7. Enter number locations Input # Locations. run experiment single location, set 1. Select serpentine cartesian Plot Order Layout. example use default cartesian layout. Enter starting plot number Starting Plot Number box. experiment multiple locations, must enter comma separated list numbers length number locations input valid. Set 101. Enter name location experiment Input Location box. multiple locations, name must comma separated list. Set \"FARGO\". ensure randomizations consistent across sessions, can set random seed box labeled random seed. example, set 1235. entered information experiment left side panel, click Run! button run design.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/rectangular_lattice.html","id":"outputs","dir":"Articles","previous_headings":"","what":"Outputs","title":"Rectangular Lattice Design","text":"run rectangular lattice design FielDHub, several ways display information contained field book.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/rectangular_lattice.html","id":"field-layout","dir":"Articles","previous_headings":"Outputs","what":"Field Layout","title":"Rectangular Lattice Design","text":"first click run button rectangular lattice design, FielDHub displays Field Layout tab, shows entries arrangement field. box display, can change layout field change location displayed. can also display heatmap field changing Type Plot Heatmap. view heatmap, must first simulate experiment described field Simulate! button. pop-window appear can enter variable want simulate along minimum maximum values.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/rectangular_lattice.html","id":"field-book","dir":"Articles","previous_headings":"Outputs","what":"Field Book","title":"Rectangular Lattice Design","text":"Field Book displays information experimental design table format. contains specific plot number row column address entry, well corresponding treatment plot. table searchable, can filter data relevant columns. simulated data heatmap, additional column variable appears field book.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/rectangular_lattice.html","id":"using-the-fieldhub-function-rectangular_lattice","dir":"Articles","previous_headings":"","what":"2. Using the FielDHub function: rectangular_lattice()","title":"Rectangular Lattice Design","text":"can run design function FielDHub package, rectangular_lattice(). First, need load FielDHub package typing, , can enter information describing design like :","code":"library(FielDHub) rect <- rectangular_lattice( t = 56, r = 3, k = 7, l = 1, plotNumber = 101, locationNames = \"FARGO\", seed = 1235 )"},{"path":"https://didiermurillof.github.io/FielDHub/articles/rectangular_lattice.html","id":"details-on-the-inputs-entered-in-rectangular_lattice-above","dir":"Articles","previous_headings":"2. Using the FielDHub function: rectangular_lattice()","what":"Details on the inputs entered in rectangular_lattice() above","title":"Rectangular Lattice Design","text":"t = 56 number treatments. r=3 number replicates. k = 7 number plots per incomplete block. l = 1 number locations. plotNumber = 101 starting plot number. locationNames = \"FARGO\" optional name location seed = 1235 random seed replicate identical randomizations.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/rectangular_lattice.html","id":"print-rect-object","dir":"Articles","previous_headings":"2. Using the FielDHub function: rectangular_lattice()","what":"Print rect object","title":"Rectangular Lattice Design","text":"","code":"print(rect) Rectangular Lattice Design Efficiency of design: Level Blocks D-Efficiency A-Efficiency A-Bound 1 1 3 1.0000000 1.0000000 1.0000000 2 2 24 0.8549751 0.8358296 0.8358296 Information on the design parameters: List of 7 $ Reps : num 3 $ iBlocks : num 8 $ NumberTreatments: num 56 $ NumberLocations : num 1 $ Locations : chr \"FARGO\" $ seed : num 1235 $ lambda : num 0.327 10 First observations of the data frame with the rectangular_lattice field book: ID LOCATION PLOT REP IBLOCK UNIT ENTRY TREATMENT 1 1 FARGO 101 1 1 1 38 G-38 2 2 FARGO 102 1 1 2 43 G-43 3 3 FARGO 103 1 1 3 2 G-2 4 4 FARGO 104 1 1 4 5 G-5 5 5 FARGO 105 1 1 5 22 G-22 6 6 FARGO 106 1 1 6 18 G-18 7 7 FARGO 107 1 1 7 15 G-15 8 8 FARGO 108 1 2 1 7 G-7 9 9 FARGO 109 1 2 2 33 G-33 10 10 FARGO 110 1 2 3 56 G-56"},{"path":"https://didiermurillof.github.io/FielDHub/articles/rectangular_lattice.html","id":"access-to-rect-object","dir":"Articles","previous_headings":"2. Using the FielDHub function: rectangular_lattice()","what":"Access to rect object","title":"Rectangular Lattice Design","text":"function rectangular_lattice() returns list consisting information displayed output tabs FielDHub app: design information, plot layout, plot numbering, entries list, field book. accessible $ operator, .e.Β rect$layoutRandom rect$fieldBook. rect$fieldBook list containing information every plot field, information location plot treatment plot. seen output , field book columns ID, LOCATION, PLOT, REP, IBLOCK, UNIT, ENTRY, TREATMENT.","code":"field_book <- rect$fieldBook head(rect$fieldBook, 10) ID LOCATION PLOT REP IBLOCK UNIT ENTRY TREATMENT 1 1 FARGO 101 1 1 1 38 G-38 2 2 FARGO 102 1 1 2 43 G-43 3 3 FARGO 103 1 1 3 2 G-2 4 4 FARGO 104 1 1 4 5 G-5 5 5 FARGO 105 1 1 5 22 G-22 6 6 FARGO 106 1 1 6 18 G-18 7 7 FARGO 107 1 1 7 15 G-15 8 8 FARGO 108 1 2 1 7 G-7 9 9 FARGO 109 1 2 2 33 G-33 10 10 FARGO 110 1 2 3 56 G-56"},{"path":"https://didiermurillof.github.io/FielDHub/articles/rectangular_lattice.html","id":"plot-the-field-layout","dir":"Articles","previous_headings":"2. Using the FielDHub function: rectangular_lattice()","what":"Plot the field layout","title":"Rectangular Lattice Design","text":"plotting layout function coordinates ROW COLUMN, can use generic function plot() follows,","code":"plot(rect)"},{"path":"https://didiermurillof.github.io/FielDHub/articles/row_column.html","id":"resolvable-row-column-design-two-stage-generation","dir":"Articles","previous_headings":"","what":"Resolvable Row-Column Design (Two Stage Generation)","title":"Row-Column Design","text":"randomly generates resolvable row-column design.design optimized rows columns blocking factors. randomization can done across multiple locations.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/row_column.html","id":"overview","dir":"Articles","previous_headings":"","what":"Overview","title":"Row-Column Design","text":"Row-Column design FielDHub built two stages. first step constructs blocking factor Columns using Incomplete Block Units incomplete block design sets number incomplete blocks number Columns design, dimension equal number Rows. design generated, Rows used Row blocking factor optimized -Efficiency, levels within original Columns fixed. optimize Rows maintaining current optimized Columns, use heuristic algorithm swaps random treatment positions within given Column (Block) also selected random. algorithm begins calculating -Efficiency initial design, performs swap iteration, recalculates -Efficiency resulting design, compares previous one decide whether keep discard new design. iterative process repeated, default, 1000 times.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/row_column.html","id":"using-the-fieldhub-shiny-app","dir":"Articles","previous_headings":"","what":"1. Using the FielDHub Shiny App","title":"Row-Column Design","text":"generate Row-Column Design using FielDHub app: First, go Designs > Resolvable Row-Column Design (RCD) , follow following steps show generate Row-Column Design 45 treatments, 5 rows 3 reps.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/row_column.html","id":"inputs","dir":"Articles","previous_headings":"","what":"Inputs","title":"Row-Column Design","text":"selection , means app going generate synthetic data entries names treatment based user inputs. selection Yes, entries list must fulfill specific format must .csv file. file must columns ENTRY NAME. ENTRY column must unique entry integer number treatment. column NAME must unique name identifies treatment. ENTRY NAME must unique, duplicates allowed. following table, show example entries list format. example entry list 12 treatments. Input number treatments Input # Treatments box. enter 45 sample experiment. Set number plots incomplete block Input # Plots per IBlock box. examples, set 5. Select number replications treatments Input # Full Reps box. examples, set 3. Enter number locations Input # Locations. run experiment single location, set 1. Select serpentine cartesian Plot Order Layout. example use default serpentine layout. Enter starting plot number Starting Plot Number box. experiment multiple locations, must enter comma separated list numbers length number locations input valid. Set 101. Enter name location experiment Input Location box. multiple locations, name must comma separated list. example, set \"FARGO\". ensure randomizations consistent across sessions, can set random seed box labeled random seed. example, set 1244. entered information experiment left side panel, click Run! button run design.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/row_column.html","id":"outputs","dir":"Articles","previous_headings":"","what":"Outputs","title":"Row-Column Design","text":"run row-column design FielDHub, several ways display information contained field book.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/row_column.html","id":"field-layout","dir":"Articles","previous_headings":"Outputs","what":"Field Layout","title":"Row-Column Design","text":"first click run button row-column design, FielDHub displays Field Layout tab, shows entries arrangement field. box display, can change layout field. can also display heatmap field changing Type Plot Heatmap. view heatmap, must first simulate experiment described field Simulate! button. pop-window appear can enter variable want simulate along minimum maximum values.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/row_column.html","id":"field-book","dir":"Articles","previous_headings":"Outputs","what":"Field Book","title":"Row-Column Design","text":"Field Book displays information experimental design table format. contains specific plot number row column address entry, well corresponding treatment plot. table searchable, can filter data relevant columns. simulated data heatmap, additional column variable appears field book.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/row_column.html","id":"using-the-fieldhub-function-row_column","dir":"Articles","previous_headings":"","what":"2. Using the FielDHub function: row_column()","title":"Row-Column Design","text":"can run design function FielDHub package, row_column(). First, need load FielDHub package typing , can enter information describing design like :","code":"library(FielDHub) rcd <- row_column( t = 45, nrows = 5, r = 3, l = 1, plotNumber = 101, locationNames = \"FARGO\", seed = 1244 )"},{"path":"https://didiermurillof.github.io/FielDHub/articles/row_column.html","id":"details-on-the-inputs-entered-in-row_column-above","dir":"Articles","previous_headings":"2. Using the FielDHub function: row_column()","what":"Details on the inputs entered in row_column() above","title":"Row-Column Design","text":"description inputs used generate design, t = 45 number treatments. nrows = 5 number rows. r=3 number reps l = 1 number locations. plotNumber = 101 starting plot number. locationNames = \"FARGO\" optional name location. seed = 1244 random seed replicate identical randomizations.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/row_column.html","id":"print-rcd-object","dir":"Articles","previous_headings":"2. Using the FielDHub function: row_column()","what":"Print rcd object","title":"Row-Column Design","text":"print summary information object rcd, can use generic function print().","code":"print(rcd) Resolvable Row-Column Design (Two Stage Generation) Efficiency of design: Level Blocks D-Efficiency A-Efficiency A-Bound 1 Rep 3 1.0000000 1.0000000 1.0000000 2 Row 15 0.8940648 0.8767593 0.8842892 3 Column 27 0.7912269 0.7624155 0.7674419 Information on the design parameters: List of 7 $ rows : num 5 $ columns : num 9 $ reps : num 3 $ treatments : num 45 $ locations : num 1 $ location_names: chr \"FARGO\" $ seed : num 1244 10 First observations of the data frame with the row_column field book: ID LOCATION PLOT REP ROW COLUMN ENTRY TREATMENT 1 1 FARGO 101 1 1 1 23 G-23 6 6 FARGO 102 1 1 2 22 G-22 11 11 FARGO 103 1 1 3 28 G-28 16 16 FARGO 104 1 1 4 1 G-1 21 21 FARGO 105 1 1 5 13 G-13 26 26 FARGO 106 1 1 6 15 G-15 31 31 FARGO 107 1 1 7 37 G-37 36 36 FARGO 108 1 1 8 42 G-42 41 41 FARGO 109 1 1 9 39 G-39 2 2 FARGO 110 1 2 1 11 G-11"},{"path":"https://didiermurillof.github.io/FielDHub/articles/row_column.html","id":"access-to-rcd-object","dir":"Articles","previous_headings":"2. Using the FielDHub function: row_column()","what":"Access to rcd object","title":"Row-Column Design","text":"row_column() function returns list consisting information displayed output tabs FielDHub app: design information, plot layout, plot numbering, entries list, field book. accessible $ operator, .e.Β rcd$layoutRandom rcd$fieldBook. rcd$fieldBook list containing information every plot field, information location plot treatment plot. seen output , field book columns ID, LOCATION, PLOT, REP, ROW, COLUMN, ENTRY, TREATMENT.","code":"field_book <- rcd$fieldBook head(rcd$fieldBook, 10) ID LOCATION PLOT REP ROW COLUMN ENTRY TREATMENT 1 1 FARGO 101 1 1 1 23 G-23 6 6 FARGO 102 1 1 2 22 G-22 11 11 FARGO 103 1 1 3 28 G-28 16 16 FARGO 104 1 1 4 1 G-1 21 21 FARGO 105 1 1 5 13 G-13 26 26 FARGO 106 1 1 6 15 G-15 31 31 FARGO 107 1 1 7 37 G-37 36 36 FARGO 108 1 1 8 42 G-42 41 41 FARGO 109 1 1 9 39 G-39 2 2 FARGO 110 1 2 1 11 G-11"},{"path":"https://didiermurillof.github.io/FielDHub/articles/row_column.html","id":"plot-the-field-layout","dir":"Articles","previous_headings":"2. Using the FielDHub function: row_column()","what":"Plot the field layout","title":"Row-Column Design","text":"plotting layout function coordinates ROW COLUMN, can use generic function plot() follows,","code":"plot(rcd)"},{"path":"https://didiermurillof.github.io/FielDHub/articles/sparse_allocation.html","id":"sparse-allocationtesting","dir":"Articles","previous_headings":"","what":"Sparse Allocation/Testing","title":"Sparse Allocation","text":"vignette shows generate un-replicated designs leveraging sparse allocation method using FielDHub Shiny App scripting function sparse_allocation() FielDHub R package.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/sparse_allocation.html","id":"overview","dir":"Articles","previous_headings":"","what":"Overview","title":"Sparse Allocation","text":"Sparse allocation valuable strategy plant breeding experiments, allows researchers evaluate large number treatments multiple locations single experiment. Sparse allocation can increase efficiency reduce number experimental units required, making cost-effective option. One standard method implementing sparse allocation plant breeding experiments incomplete block designs (IBD) (Edmondson 2020). following key points summarize advantages disadvantages sparse allocation (Montesinos-Lopez et al. 2022): Increased efficiency: using sparse allocation, breeders can evaluate large number genotypes treatments single experiment across multiple environments, can accelerate breeding program reduce time resources needed evaluation. Selection intensity: large number genotypes treatments evaluated sparse allocation experiments can increase genetic diversity breeding program increase chances identifying superior genotypes treatments. Cost-effective: Sparse allocation experiments generally less expensive compared fully replicated experiments since fewer experimental units needed. Less accurate predictions: limited number experimental units means estimates treatment effects less precise compared fully replicated designs. However, increase selection intensity may compensate loss accuracy (Trade problem). FielDHub includes function run sparse allocation strategy multi-location randomization, well interface creating sparse allocation design FielDHub app.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/sparse_allocation.html","id":"use-case","dir":"Articles","previous_headings":"","what":"Use Case","title":"Sparse Allocation","text":"plant breeding project aims test 260 entries across five environments, due limited seed availability, four replications genotype can created across five locations. result, genotypes present environments. Additionally, project includes four checks replicated environments. address seed shortage, sparse allocation strategy used. table illustrates allocation first ten genotypes across five environments. table illustrates allocation genotypes across different environments, genotypes listed rows environments columns. Specifically, indicates Genotype 1 (Gen-1) assigned locations 1, 2, 3, 4, environment 5. process allocating genotypes locations achieved optimization process employs IBD principles.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/sparse_allocation.html","id":"running-the-shiny-app","dir":"Articles","previous_headings":"","what":"Running the Shiny App","title":"Sparse Allocation","text":"launch app need run either ","code":"FielDHub::run_app() library(FielDHub) run_app()"},{"path":"https://didiermurillof.github.io/FielDHub/articles/sparse_allocation.html","id":"using-the-fieldhub-shiny-app","dir":"Articles","previous_headings":"","what":"1. Using the FielDHub Shiny App","title":"Sparse Allocation","text":"app running, go Unreplicated Designs > Sparse Allocation , follow following steps show generate sparse allocation experiment.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/sparse_allocation.html","id":"inputs","dir":"Articles","previous_headings":"","what":"Inputs","title":"Sparse Allocation","text":"Import entries’ list? Choose whether import list entry numbers names genotypes treatments. selection , app generate synthetic data entries names treatment/genotypes based user inputs. selection Yes, entries list must fulfill specific format must .csv file. file must columns ENTRY NAME. ENTRY column must unique integer number entry treatment/genotype. column NAME must unique name identifies treatment/genotype. ENTRY NAME must unique, duplicates allowed. following table shows example entries list format. checks must appear first rows .csv file. Enter number entries/treatments Input # Entries box, 260 case. Select 4 drop-Input # Checks box. Since want run experiment 5 locations, set Input # Locations 5. Set number copies treatment # Copies Per Entry dropdown box 4. Select serpentine cartesian Plot Order Layout. example use serpentine layout. ensure randomizations consistent across sessions, can set seed number box labeled Random Seed. instance, set 16. Enter name experiment Input Experiment Name box. example, SparseTest2023. Enter starting plot number Starting Plot Number box. experiment want plot start 1, 1001, 2001, 3001, 4001 location. Enter name site/location Input Location box. experiment set sites FARGO, CASSELTON, MINOT, PROSPER, WILLISTON. entered information experiment left side panel, click Run! button run design. prompted select dimensions field list options drop-middle screen box labeled Select dimensions field. case, select 16 x 15. also can see table sparse allocation. Click Randomize! button randomize experiment set field dimensions see output plots. change inputs left side panel running experiment initially, click Run Randomize buttons , re-run new inputs.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/sparse_allocation.html","id":"outputs","dir":"Articles","previous_headings":"","what":"Outputs","title":"Sparse Allocation","text":"run sparse allocation design FielDHub set dimensions field, several ways display information sparse process randomization.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/sparse_allocation.html","id":"expt-design-info","dir":"Articles","previous_headings":"Outputs","what":"Expt Design Info","title":"Sparse Allocation","text":"first tab, Expt Design Info, can see entries randomization displayed binary matrix column location, 1 indicating respective genotype respective location, 0 indicating . sparse genotype allocation characteristic method. buttons copy, print, save table Excel file.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/sparse_allocation.html","id":"randomized-field","dir":"Articles","previous_headings":"Outputs","what":"Randomized Field","title":"Sparse Allocation","text":"Randomized Field tab displays graphical representation randomization entries field specified dimensions. checks colored uniquely, showing number times distributed throughout field. display includes numbered labels rows columns. can copy field table save directly Excel file Copy Excel buttons top. Choose % Checks: drop-box, users can play different options total amount checks field.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/sparse_allocation.html","id":"plot-number-field","dir":"Articles","previous_headings":"Outputs","what":"Plot Number Field","title":"Sparse Allocation","text":"Plot Number Field tab, table display field plots numbered according Plot Order Layout specified, either serpentine cartesian. can see corresponding entries plot number field book. Like Randomized Field tab, can copy table save Excel file Copy Excel buttons.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/sparse_allocation.html","id":"field-book","dir":"Articles","previous_headings":"Outputs","what":"Field Book","title":"Sparse Allocation","text":"Field Book displays information experimental design table format. contains specific plot number row column address entry, well corresponding treatment plot. table searchable, can filter data relevant columns.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/sparse_allocation.html","id":"using-the-fieldhub-function-sparse_allocation","dir":"Articles","previous_headings":"","what":"2. Using the FielDHub function: sparse_allocation()","title":"Sparse Allocation","text":"can run design function FielDHub package, sparse_allocation(). First, need load FielDHub package typing, , can enter information describing design like :","code":"library(FielDHub) sparse_example <- sparse_allocation( lines = 260, l = 5, copies_per_entry = 4, checks = 4, plotNumber = c(1, 1001, 2001, 3001, 4001), locationNames = c(\"FARGO\", \"CASSELTON\", \"MINOT\", \"PROSPER\", \"WILLISTON\"), exptName = \"SparseTest2023\", seed = 16 )"},{"path":"https://didiermurillof.github.io/FielDHub/articles/sparse_allocation.html","id":"details-on-the-inputs-entered-in-sparse_allocation-above","dir":"Articles","previous_headings":"2. Using the FielDHub function: sparse_allocation()","what":"Details on the inputs entered in sparse_allocation() above:","title":"Sparse Allocation","text":"lines = 260 number genotypes. l = 5 number locations. copies_per_entry = 4 number copies entry. checks = 4 number checks. plotNumber = c(1, 1001, 2001, 3001, 4001) optional starting plot numbers locationNames = c(\"FARGO\", \"CASSELTON\", \"MINOT\", \"PROSPER\", \"WILLISTON\") optional names location. exptName = \"SparseTest2023\" optional name experiment seed = 16 random seed number replicate identical randomizations.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/sparse_allocation.html","id":"print-sparse_example-object","dir":"Articles","previous_headings":"2. Using the FielDHub function: sparse_allocation()","what":"Print sparse_example object","title":"Sparse Allocation","text":"print summary information object sparse_example, can use generic function print(). sparse_allocation() function returns list objects, includes outputs function diagonal_arrangement() addition list_locs, allocation, size_locations. list_locs object list data frames. data frame two columns; ENTRY NAME information randomize environment. object allocation binary allocation matrix genotypes locations, size_locations data frame column location row indicating size location (number field plots). example, can display allocation object. Let us print first ten genotypes allocation. can manipulate sparse_allocation object list R. example, can print design information following: outputs:","code":"print(head(sparse_example$allocation, 10)) LOC1 LOC2 LOC3 LOC4 LOC5 1 1 1 0 1 1 2 1 1 1 0 1 3 1 1 0 1 1 4 1 1 1 1 0 5 1 1 1 0 1 6 1 1 0 1 1 7 1 0 1 1 1 8 0 1 1 1 1 9 1 1 1 1 0 10 1 1 1 0 1 print(sparse_example) Sparse Allocation: Un-replicated Diagonal Arrangement Design Information on the design parameters: List of 11 $ rows : num 15 $ columns : num 16 $ treatments : int 208 $ checks : int 4 $ entry_checks :List of 5 ..$ : num [1:4] 261 262 263 264 ..$ : num [1:4] 261 262 263 264 ..$ : num [1:4] 261 262 263 264 ..$ : num [1:4] 261 262 263 264 ..$ : num [1:4] 261 262 263 264 $ rep_checks :List of 5 ..$ : num [1:4] 8 7 7 8 ..$ : num [1:4] 8 8 7 7 ..$ : num [1:4] 7 7 8 8 ..$ : num [1:4] 7 8 7 8 ..$ : num [1:4] 8 7 7 8 $ locations : num 5 $ planter : chr \"serpentine\" $ percent_checks: chr [1:5] \"12.5%\" \"12.5%\" \"12.5%\" \"12.5%\" ... $ fillers : int 2 $ seed : num 16 10 First observations of the data frame with the diagonal_arrangement field book: ID EXPT LOCATION YEAR PLOT ROW COLUMN CHECKS ENTRY TREATMENT 1 1 SparseTest2023 FARGO 2024 1 1 1 0 34 G-34 2 2 SparseTest2023 FARGO 2024 2 1 2 0 83 G-83 3 3 SparseTest2023 FARGO 2024 3 1 3 0 59 G-59 4 4 SparseTest2023 FARGO 2024 4 1 4 261 261 CH-261 5 5 SparseTest2023 FARGO 2024 5 1 5 0 220 G-220 6 6 SparseTest2023 FARGO 2024 6 1 6 0 65 G-65 7 7 SparseTest2023 FARGO 2024 7 1 7 0 188 G-188 8 8 SparseTest2023 FARGO 2024 8 1 8 0 22 G-22 9 9 SparseTest2023 FARGO 2024 9 1 9 0 235 G-235 10 10 SparseTest2023 FARGO 2024 10 1 10 0 238 G-238"},{"path":"https://didiermurillof.github.io/FielDHub/articles/sparse_allocation.html","id":"access-to-sparse_example-object","dir":"Articles","previous_headings":"2. Using the FielDHub function: sparse_allocation()","what":"Access to sparse_example object","title":"Sparse Allocation","text":"object sparse_example list consisting information displayed output tabs FielDHub app: design information, plot layout, plot numbering, entries list, field book, indexed location experiment. accessible $ operator, .e.Β designs$layoutRandom[[1]] LOC1 designs$fieldBook whole field book. designs$fieldBook data frame containing information every plot field, information location plot treatment plot. seen output , field book columns ID, EXPT, LOCATION, YEAR, PLOT, ROW, COLUMN, CHECKS, ENTRY, TREATMENT. Let us see first 10 rows field book first location experiment.","code":"field_book <- sparse_example$fieldBook head(field_book, 10) ID EXPT LOCATION YEAR PLOT ROW COLUMN CHECKS ENTRY TREATMENT 1 1 SparseTest2023 FARGO 2024 1 1 1 0 34 G-34 2 2 SparseTest2023 FARGO 2024 2 1 2 0 83 G-83 3 3 SparseTest2023 FARGO 2024 3 1 3 0 59 G-59 4 4 SparseTest2023 FARGO 2024 4 1 4 261 261 CH-261 5 5 SparseTest2023 FARGO 2024 5 1 5 0 220 G-220 6 6 SparseTest2023 FARGO 2024 6 1 6 0 65 G-65 7 7 SparseTest2023 FARGO 2024 7 1 7 0 188 G-188 8 8 SparseTest2023 FARGO 2024 8 1 8 0 22 G-22 9 9 SparseTest2023 FARGO 2024 9 1 9 0 235 G-235 10 10 SparseTest2023 FARGO 2024 10 1 10 0 238 G-238"},{"path":[]},{"path":"https://didiermurillof.github.io/FielDHub/articles/sparse_allocation.html","id":"plot-field-layout-location-1","dir":"Articles","previous_headings":"2. Using the FielDHub function: sparse_allocation() > Plot field layout","what":"Plot field layout Location 1","title":"Sparse Allocation","text":"plotting layout function coordinates ROW COLUMN field book object can use generic function plot() follows, plot LOC1. can plot location experiment, like location 2 example:","code":"plot(sparse_example, l = 1)"},{"path":"https://didiermurillof.github.io/FielDHub/articles/sparse_allocation.html","id":"plot-field-layout-location-2","dir":"Articles","previous_headings":"2. Using the FielDHub function: sparse_allocation() > Plot field layout","what":"Plot field layout Location 2","title":"Sparse Allocation","text":"figure shows map experiment randomized unreplicated arrangement design. blue plots represent unreplicated treatments, yellow-boxed colored check plots replicated throughout field systematic diagonal arrangement. red plots 0s fillers.","code":"plot(sparse_example, l = 2)"},{"path":[]},{"path":"https://didiermurillof.github.io/FielDHub/articles/split_plot.html","id":"using-the-fieldhub-shiny-app","dir":"Articles","previous_headings":"","what":"1. Using the FielDHub Shiny App","title":"Split-Plot Design","text":"launch app need run either app running, go Designs > Split-Plot Design , follow following steps show generate kind design example 3 whole plots, 2 sub-plots 3 reps. run experiment just one location.","code":"FielDHub::run_app() library(FielDHub) run_app()"},{"path":"https://didiermurillof.github.io/FielDHub/articles/split_plot.html","id":"inputs","dir":"Articles","previous_headings":"","what":"Inputs","title":"Split-Plot Design","text":"selection , means app going generate synthetic data entries names treatment/genotypes based user inputs. selection Yes, entries list must fulfill specific format must .csv file. file must single column TREATMENT, containing list unique names identify treatment/genotype. Duplicate values allowed, entries must unique. following, show example entries list format. example entry list 5 whole-plots 3 sub-plots. Choose whether use split-plot design RCBD CRD Select SPD Type box. Set number whole-plots design Whole-plots box. Set 5. Set number sub-plots contained Sub-plots Within Whole-plots box. Set 3. Select number replications treatments Input # Full Reps box. Set 4. Enter number locations Input # Locations. run experiment single location, set 1. Select serpentine cartesian Plot Order Layout. example use default serpentine layout. Enter starting plot number Starting Plot Number box. experiment multiple locations, must enter comma separated list numbers length number locations input valid. case, set 101. Enter name location experiment Input Location box. multiple locations, name must comma separated list. Set \"FARGO\". ensure randomizations consistent across sessions, can set random seed box labeled random seed. example, set 1237. entered information experiment left side panel, click Run! button run design.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/split_plot.html","id":"outputs","dir":"Articles","previous_headings":"Inputs","what":"Outputs","title":"Split-Plot Design","text":"run split-plot design FielDHub, several ways display information contained field book.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/split_plot.html","id":"field-layout","dir":"Articles","previous_headings":"Inputs > Outputs","what":"Field Layout","title":"Split-Plot Design","text":"first click run button split-plot design, FielDHub displays Field Layout tab, shows entries arrangement field. box display, can change layout field. can also display heatmap field changing Type Plot Heatmap. view heatmap, must first simulate experiment described field Simulate! button. pop-window appear can enter variable want simulate along minimum maximum values.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/split_plot.html","id":"field-book","dir":"Articles","previous_headings":"Inputs > Outputs","what":"Field Book","title":"Split-Plot Design","text":"Field Book displays information experimental design table format. contains specific plot number row column address entry, well corresponding treatment plot. table searchable, can filter data relevant columns. simulated data heatmap, additional column variable appears field book.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/split_plot.html","id":"using-the-fieldhub-function-split_plot","dir":"Articles","previous_headings":"Inputs","what":"2. Using the FielDHub function: split_plot()","title":"Split-Plot Design","text":"can run design function FielDHub package, split_plot(). First, need load FielDHub package typing , can enter information describing design like :","code":"library(FielDHub) spd <- split_plot( wp = 5, sp = 3, reps = 4, type = 2, plotNumber = 101, locationNames = \"FARGO\", l = 1, seed = 1240 )"},{"path":"https://didiermurillof.github.io/FielDHub/articles/split_plot.html","id":"details-on-the-inputs-entered-in-split_plot-above","dir":"Articles","previous_headings":"Inputs > 2. Using the FielDHub function: split_plot()","what":"Details on the inputs entered in split_plot() above","title":"Split-Plot Design","text":"description inputs used generate design, wp = 5 number whole-plots. sp = 3 number sub-plots. reps = 4 number reps type = 2 CRD RCBD, 1 2 respectively l = 1 number locations. plotNumber = 101 starting plot number. locationNames = \"FARGO\" optional name location. seed = 1240 random seed replicate identical randomizations.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/split_plot.html","id":"print-spd-object","dir":"Articles","previous_headings":"Inputs > 2. Using the FielDHub function: split_plot()","what":"Print spd object","title":"Split-Plot Design","text":"","code":"print(spd) Split Plot Design Information on the design parameters: List of 7 $ WholePlots : int [1:5] 1 2 3 4 5 $ SubPlots : int [1:3] 1 2 3 $ locationNumber: num 1 $ locationNames : chr \"FARGO\" $ plotNumbers : num 101 $ typeDesign : chr \"RCBD\" $ seed : num 1240 10 First observations of the data frame with the split_plot field book: ID LOCATION PLOT REP WHOLE_PLOT SUB_PLOT TRT_COMB 1 1 FARGO 101 1 2 2 2|2 2 2 FARGO 101 1 2 1 2|1 3 3 FARGO 101 1 2 3 2|3 4 4 FARGO 102 1 4 3 4|3 5 5 FARGO 102 1 4 2 4|2 6 6 FARGO 102 1 4 1 4|1 7 7 FARGO 103 1 1 1 1|1 8 8 FARGO 103 1 1 3 1|3 9 9 FARGO 103 1 1 2 1|2 10 10 FARGO 104 1 3 3 3|3"},{"path":"https://didiermurillof.github.io/FielDHub/articles/split_plot.html","id":"access-to-spd-object","dir":"Articles","previous_headings":"Inputs > 2. Using the FielDHub function: split_plot()","what":"Access to spd object","title":"Split-Plot Design","text":"split_plot() function returns list consisting information displayed output tabs FielDHub app: design information, plot layout, plot numbering, entries list, field book. accessible $ operator, .e.Β spd$layoutRandom spd$fieldBook. spd$fieldBook list containing information every plot field, information location plot treatment plot. seen output , field book columns ID, LOCATION, PLOT, REP, WHOLE_PLOT, SUB_PLOT, TRT_COMB.","code":"field_book <- spd$fieldBook head(spd$fieldBook, 10) ID LOCATION PLOT REP WHOLE_PLOT SUB_PLOT TRT_COMB 1 1 FARGO 101 1 2 2 2|2 2 2 FARGO 101 1 2 1 2|1 3 3 FARGO 101 1 2 3 2|3 4 4 FARGO 102 1 4 3 4|3 5 5 FARGO 102 1 4 2 4|2 6 6 FARGO 102 1 4 1 4|1 7 7 FARGO 103 1 1 1 1|1 8 8 FARGO 103 1 1 3 1|3 9 9 FARGO 103 1 1 2 1|2 10 10 FARGO 104 1 3 3 3|3"},{"path":"https://didiermurillof.github.io/FielDHub/articles/split_plot.html","id":"plot-the-field-layout","dir":"Articles","previous_headings":"Inputs > 2. Using the FielDHub function: split_plot()","what":"Plot the field layout","title":"Split-Plot Design","text":"plotting layout function coordinates ROW COLUMN, can use generic function plot() follows,","code":"plot(spd)"},{"path":"https://didiermurillof.github.io/FielDHub/articles/split_split_plot.html","id":"using-the-fieldhub-shiny-app","dir":"Articles","previous_headings":"","what":"1. Using the FielDHub Shiny App","title":"Split-Split Plot Design","text":"launch app need run either app running, go Designs > Split-Split Plot Design , follow following steps show generate kind design example 3 whole plots, 2 sub-plots, 4 sub-sub plots 3 reps. run experiment just one location.","code":"FielDHub::run_app() library(FielDHub) run_app()"},{"path":"https://didiermurillof.github.io/FielDHub/articles/split_split_plot.html","id":"inputs","dir":"Articles","previous_headings":"","what":"Inputs","title":"Split-Split Plot Design","text":"selection , means app going generate synthetic data entries names treatment based user inputs. selection Yes, entries list must fulfill specific format must .csv file. file must single column TREATMENT, containing list unique names identify treatment. Duplicate values allowed, entries must unique. following, show example entries list format. example entry list 10 treatments. Choose whether use split-plot design RCBD CRD Select SPD Type box. Set number whole-plots design Whole-plots box. Set 3. Set number sub-plots contained Sub-plots Within Whole-plots box. Set 2. Set number sub-sub plots contained Sub-Sub-plots within Sub-plots box. Set 4. Select number replications treatments Input # Full Reps box. Set 3. Enter number locations Input # Locations. run experiment single location, set 1. Select serpentine cartesian Plot Order Layout. example use default serpentine layout. Enter starting plot number Starting Plot Number box. experiment multiple locations, must enter comma separated list numbers length number locations input valid. case, set 101. Enter name location experiment Input Location box. multiple locations, name must comma separated list. Set \"FARGO\". ensure randomizations consistent across sessions, can set random seed box labeled random seed. example, set 1238. entered information experiment left side panel, click Run! button run design.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/split_split_plot.html","id":"outputs","dir":"Articles","previous_headings":"Inputs","what":"Outputs","title":"Split-Split Plot Design","text":"run split-split-plot design FielDHub, several ways display information contained field book.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/split_split_plot.html","id":"field-layout","dir":"Articles","previous_headings":"Inputs > Outputs","what":"Field Layout","title":"Split-Split Plot Design","text":"first click run button split-split-plot design, FielDHub displays Field Layout tab, shows entries arrangement field. box display, can change layout field. can also display heatmap field changing Type Plot Heatmap. view heatmap, must first simulate experiment described field Simulate! button. pop-window appear can enter variable want simulate along minimum maximum values.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/split_split_plot.html","id":"field-book","dir":"Articles","previous_headings":"Inputs > Outputs","what":"Field Book","title":"Split-Split Plot Design","text":"Field Book displays information experimental design table format. contains specific plot number row column address entry, well corresponding treatment plot. table searchable, can filter data relevant columns. simulated data heatmap, additional column variable appears field book.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/split_split_plot.html","id":"using-the-fieldhub-function-split_split_plot","dir":"Articles","previous_headings":"Inputs","what":"2. Using the FielDHub function: split_split_plot()","title":"Split-Split Plot Design","text":"can run design function FielDHub package, split_split_plot(). First, need load FielDHub package typing, , can enter information describing design like :","code":"library(FielDHub) sspd <- split_split_plot( wp = 3, sp = 2, ssp = 4, reps = 3, type = 2, l = 1, plotNumber = 101, locationNames = \"FARGO\", seed = 123 )"},{"path":"https://didiermurillof.github.io/FielDHub/articles/split_split_plot.html","id":"details-on-the-inputs-entered-in-split_split_plot-above","dir":"Articles","previous_headings":"Inputs > 2. Using the FielDHub function: split_split_plot()","what":"Details on the inputs entered in split_split_plot() above","title":"Split-Split Plot Design","text":"description inputs used generate design, wp = 3 number whole-plots. sp = 2 number sub-plots. ssp = 4 number sub-sub-plots. reps = 3 number reps type = 2 CRD RCBD, 1 2 respectively l = 1 number locations. plotNumber = 101 starting plot number. locationNames = \"FARGO\" optional name location. seed = 1240 random seed replicate identical randomizations.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/split_split_plot.html","id":"print-sspd-object","dir":"Articles","previous_headings":"Inputs > 2. Using the FielDHub function: split_split_plot()","what":"Print sspd object","title":"Split-Split Plot Design","text":"","code":"print(sspd) Split-Split Plot Design Information on the design parameters: List of 6 $ Whole.Plots : int [1:3] 1 2 3 $ Sub.Plots : int [1:2] 1 2 $ Sub.Sub.Plots: int [1:4] 1 2 3 4 $ Locations : num 1 $ typeDesign : chr \"RCBD\" $ seed : num 123 10 First observations of the data frame with the split_split_plot field book: ID LOCATION PLOT REP WHOLE_PLOT SUB_PLOT SUB_SUB_PLOT TRT_COMB 1 1 FARGO 101 1 3 1 2 3|1|2 2 2 FARGO 101 1 3 1 3 3|1|3 3 3 FARGO 101 1 3 1 4 3|1|4 4 4 FARGO 101 1 3 1 1 3|1|1 5 5 FARGO 101 1 3 2 2 3|2|2 6 6 FARGO 101 1 3 2 3 3|2|3 7 7 FARGO 101 1 3 2 4 3|2|4 8 8 FARGO 101 1 3 2 1 3|2|1 9 9 FARGO 102 1 1 2 1 1|2|1 10 10 FARGO 102 1 1 2 3 1|2|3"},{"path":"https://didiermurillof.github.io/FielDHub/articles/split_split_plot.html","id":"access-to-sspd-object","dir":"Articles","previous_headings":"Inputs > 2. Using the FielDHub function: split_split_plot()","what":"Access to sspd object","title":"Split-Split Plot Design","text":"split_split_plot() function returns list consisting information displayed output tabs FielDHub app: design information, plot layout, plot numbering, entries list, field book. accessible $ operator, .e.Β sspd$layoutRandom sspd$fieldBook. sspd$fieldBook list containing information every plot field, information location plot treatment plot. seen output , field book columns ID, LOCATION, PLOT, REP, WHOLE_PLOT, SUB_PLOT, SUB_SUB_PLOT, TRT_COMB.","code":"field_book <- sspd$fieldBook head(field_book,10) ID LOCATION PLOT REP WHOLE_PLOT SUB_PLOT SUB_SUB_PLOT TRT_COMB 1 1 FARGO 101 1 3 1 2 3|1|2 2 2 FARGO 101 1 3 1 3 3|1|3 3 3 FARGO 101 1 3 1 4 3|1|4 4 4 FARGO 101 1 3 1 1 3|1|1 5 5 FARGO 101 1 3 2 2 3|2|2 6 6 FARGO 101 1 3 2 3 3|2|3 7 7 FARGO 101 1 3 2 4 3|2|4 8 8 FARGO 101 1 3 2 1 3|2|1 9 9 FARGO 102 1 1 2 1 1|2|1 10 10 FARGO 102 1 1 2 3 1|2|3"},{"path":"https://didiermurillof.github.io/FielDHub/articles/split_split_plot.html","id":"plot-the-field-layout","dir":"Articles","previous_headings":"Inputs > 2. Using the FielDHub function: split_split_plot()","what":"Plot the field layout","title":"Split-Split Plot Design","text":"plotting layout function coordinates ROW COLUMN, can use generic function plot() follows,","code":"plot(sspd)"},{"path":"https://didiermurillof.github.io/FielDHub/articles/square_lattice.html","id":"using-the-fieldhub-shiny-app","dir":"Articles","previous_headings":"","what":"1. Using the FielDHub Shiny App","title":"Square Lattice Design","text":"generate square lattice design using FielDHub app: First, go Lattice Designs > Square Lattice , follow following steps show generate square lattice design 64 treatments 3 reps.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/square_lattice.html","id":"inputs","dir":"Articles","previous_headings":"","what":"Inputs","title":"Square Lattice Design","text":"selection , means app going generate synthetic data entries names treatment/genotypes based user inputs. selection Yes, entries list must fulfill specific format must .csv file. file must columns ENTRY NAME. ENTRY column must unique entry integer number treatment. column NAME must unique name identifies treatment. ENTRY NAME must unique, duplicates allowed. following table, show example entries list format. example entry list 12 treatments. Input number treatments Input # Treatments box. number treatments must square number square lattice design. enter 64 sample experiment. Select number replications treatments Input # Full Reps box. examples, set 3. Set number plots incomplete block Input # Plots per IBlock box. examples, set 8. Enter number locations Input # Locations. run experiment single location, set 1. Select serpentine cartesian Plot Order Layout. example use default serpentine layout. Enter starting plot number Starting Plot Number box. experiment multiple locations, must enter comma separated list numbers length number locations input valid. Set 101. Enter name location experiment Input Location box. multiple locations, name must comma separated list. example, set \"FARGO\". ensure randomizations consistent across sessions, can set random seed box labeled random seed. example, set 1233. entered information experiment left side panel, click Run! button run design.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/square_lattice.html","id":"outputs","dir":"Articles","previous_headings":"","what":"Outputs","title":"Square Lattice Design","text":"run square lattice design FielDHub, several ways display information contained field book.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/square_lattice.html","id":"field-layout","dir":"Articles","previous_headings":"Outputs","what":"Field Layout","title":"Square Lattice Design","text":"first click run button square lattice design, FielDHub displays Field Layout tab, shows entries arrangement field. box display, can change layout field change location displayed. can also display heatmap field changing Type Plot Heatmap. view heatmap, must first simulate experiment described field Simulate! button. pop-window appear can enter variable want simulate along minimum maximum values.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/square_lattice.html","id":"field-book","dir":"Articles","previous_headings":"Outputs","what":"Field Book","title":"Square Lattice Design","text":"Field Book displays information experimental design table format. contains specific plot number row column address entry, well corresponding treatment plot. table searchable, can filter data relevant columns. simulated data heatmap, additional column variable appears field book.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/square_lattice.html","id":"using-the-fieldhub-function-square_lattice","dir":"Articles","previous_headings":"","what":"2. Using the FielDHub function: square_lattice()","title":"Square Lattice Design","text":"can run design function FielDHub package, square_lattice(). First, need load FielDHub package typing, , can enter information describing design like :","code":"library(FielDHub) square <- square_lattice( t = 64, r = 3, k = 8, l = 1, plotNumber = 101, locationNames = \"FARGO\", seed = 1233 )"},{"path":"https://didiermurillof.github.io/FielDHub/articles/square_lattice.html","id":"details-on-the-inputs-entered-in-square_lattice-above","dir":"Articles","previous_headings":"2. Using the FielDHub function: square_lattice()","what":"Details on the inputs entered in square_lattice() above","title":"Square Lattice Design","text":"description inputs used generate design, t = 64 number treatments, must square number. r=3 number replicates. k = 8 number plots per incomplete block. l = 1 number locations. plotNumber = 101 starting plot number. locationNames = \"FARGO\" optional name location. seed = 1233 random seed replicate identical randomizations.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/square_lattice.html","id":"print-square-object","dir":"Articles","previous_headings":"2. Using the FielDHub function: square_lattice()","what":"Print square object","title":"Square Lattice Design","text":"print summary information object square, can use generic function print().","code":"print(square) Square Lattice Design Efficiency of design: Level Blocks D-Efficiency A-Efficiency A-Bound 1 1 3 1.0000000 1.0000000 1.0000000 2 2 24 0.8735805 0.8571429 0.8571429 Information on the design parameters: List of 7 $ Reps : num 3 $ IBlocks : num 8 $ NumberTreatments: num 64 $ NumberLocations : num 1 $ Locations : chr \"FARGO\" $ seed : num 1233 $ lambda : num 0.333 10 First observations of the data frame with the square_lattice field book: ID LOCATION PLOT REP IBLOCK UNIT ENTRY TREATMENT 1 1 FARGO 101 1 1 1 3 G-3 2 2 FARGO 102 1 1 2 38 G-38 3 3 FARGO 103 1 1 3 37 G-37 4 4 FARGO 104 1 1 4 24 G-24 5 5 FARGO 105 1 1 5 40 G-40 6 6 FARGO 106 1 1 6 29 G-29 7 7 FARGO 107 1 1 7 25 G-25 8 8 FARGO 108 1 1 8 53 G-53 9 9 FARGO 109 1 2 1 61 G-61 10 10 FARGO 110 1 2 2 23 G-23"},{"path":"https://didiermurillof.github.io/FielDHub/articles/square_lattice.html","id":"access-to-square-object","dir":"Articles","previous_headings":"2. Using the FielDHub function: square_lattice()","what":"Access to square object","title":"Square Lattice Design","text":"square_lattice() function returns list consisting information displayed output tabs FielDHub app: design information, plot layout, plot numbering, entries list, field book. accessible $ operator, .e.Β square$layoutRandom square$fieldBook. square$fieldBook list containing information every plot field, information location plot treatment plot. seen output , field book columns ID, LOCATION, PLOT, REP, IBLOCK, UNIT, ENTRY, TREATMENT.","code":"field_book <- square$fieldBook head(square$fieldBook, 10) ID LOCATION PLOT REP IBLOCK UNIT ENTRY TREATMENT 1 1 FARGO 101 1 1 1 3 G-3 2 2 FARGO 102 1 1 2 38 G-38 3 3 FARGO 103 1 1 3 37 G-37 4 4 FARGO 104 1 1 4 24 G-24 5 5 FARGO 105 1 1 5 40 G-40 6 6 FARGO 106 1 1 6 29 G-29 7 7 FARGO 107 1 1 7 25 G-25 8 8 FARGO 108 1 1 8 53 G-53 9 9 FARGO 109 1 2 1 61 G-61 10 10 FARGO 110 1 2 2 23 G-23"},{"path":"https://didiermurillof.github.io/FielDHub/articles/square_lattice.html","id":"plot-the-field-layout","dir":"Articles","previous_headings":"2. Using the FielDHub function: square_lattice()","what":"Plot the field layout","title":"Square Lattice Design","text":"plotting layout function coordinates ROW COLUMN, can use generic function plot() follows,","code":"plot(square)"},{"path":"https://didiermurillof.github.io/FielDHub/articles/strip_plot.html","id":"using-the-fieldhub-shiny-app","dir":"Articles","previous_headings":"","what":"1. Using the FielDHub Shiny App","title":"Strip-Plot Design","text":"launch app need run either app running, go Designs > Strip-Plot Design , follow following steps show generate kind design example 6 factors horizontal strips, 4 factors vertical strips 3 reps. run experiment just one location.","code":"FielDHub::run_app() library(FielDHub) run_app()"},{"path":"https://didiermurillof.github.io/FielDHub/articles/strip_plot.html","id":"inputs","dir":"Articles","previous_headings":"","what":"Inputs","title":"Strip-Plot Design","text":"selection , means app going generate synthetic data entries names treatment based user inputs. selection Yes, entries list must fulfill specific format must .csv file. file must single column TREATMENT, containing list unique names identify treatment. Duplicate values allowed, entries must unique. following, show example entries list format. example entry list 10 treatments. Input number factors horizontal strips Input # Horizontal Strips box. Set 6. Input number factors vertical strips Input # Vertical Strips box. Set 4. Select number replications experiment Input # Full Reps box. Set 3. Enter number locations Input # Locations. run experiment single location, set 1. Select serpentine cartesian Plot Order Layout. example use default serpentine layout. Enter starting plot number Starting Plot Number box. experiment multiple locations, must enter comma separated list numbers length number locations input valid. case, set 101. Enter name location experiment Input Location box. multiple locations, name must comma separated list. Set \"FARGO\". ensure randomizations consistent across sessions, can set random seed box labeled random seed. example, set 1237. entered information experiment left side panel, click Run! button run design.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/strip_plot.html","id":"outputs","dir":"Articles","previous_headings":"","what":"Outputs","title":"Strip-Plot Design","text":"run strip-plot design FielDHub, several ways display information contained field book.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/strip_plot.html","id":"field-layout","dir":"Articles","previous_headings":"Outputs","what":"Field Layout","title":"Strip-Plot Design","text":"first click run button strip-plot design, FielDHub displays Field Layout tab, shows entries arrangement field. box display, can change layout field. can also display heatmap field changing Type Plot Heatmap. view heatmap, must first simulate experiment described field Simulate! button. pop-window appear can enter variable want simulate along minimum maximum values.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/strip_plot.html","id":"field-book","dir":"Articles","previous_headings":"Outputs","what":"Field Book","title":"Strip-Plot Design","text":"Field Book displays information experimental design table format. contains specific plot number row column address entry, well corresponding treatment plot. table searchable, can filter data relevant columns. simulated data heatmap, additional column variable appears Field Book.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/strip_plot.html","id":"using-the-fieldhub-function-strip_plot","dir":"Articles","previous_headings":"","what":"2. Using the FielDHub function: strip_plot()","title":"Strip-Plot Design","text":"can run design function FielDHub package, strip_plot(). can enter information describing design like : can run design function FielDHub package, strip_plot(). First, need load FielDHub package typing, , can enter information describing design like :","code":"library(FielDHub) strip <- strip_plot( Hplots = 6, Vplots = 4, b = 3, l = 1, plotNumber = 101, planter = \"serpentine\", locationNames = \"FARGO\", seed = 1240 )"},{"path":"https://didiermurillof.github.io/FielDHub/articles/strip_plot.html","id":"details-on-the-inputs-entered-in-strip_plot-above","dir":"Articles","previous_headings":"2. Using the FielDHub function: strip_plot()","what":"Details on the inputs entered in strip_plot() above","title":"Strip-Plot Design","text":"description inputs used generate design, Hplots = 6 number horizontal strips Vplots = 4 number vertical strips b = 3 number reps l = 1 number locations. plotNumber = 101 starting plot number. planter = \"cartesian\" order layout. locationNames = \"FARGO\" optional name location. seed = 1240 random seed replicate identical randomizations.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/strip_plot.html","id":"print-strip-object","dir":"Articles","previous_headings":"2. Using the FielDHub function: strip_plot()","what":"Print strip object","title":"Strip-Plot Design","text":"","code":"print(strip) Strip Plot Design Information on the design parameters: List of 6 $ Hplots : int 6 $ Vplots : int 4 $ blocks : num 3 $ numberLocations: num 1 $ nameLocations : chr \"FARGO\" $ seed : num 1240 10 First observations of the data frame with the strip_plot field book: ID LOCATION PLOT REP HSTRIP VSTRIP TRT_COMB 1 1 FARGO 101 1 b1 a0 b1|a0 2 2 FARGO 102 1 b1 a1 b1|a1 3 3 FARGO 103 1 b1 a3 b1|a3 4 4 FARGO 104 1 b1 a2 b1|a2 5 5 FARGO 108 1 b3 a0 b3|a0 6 6 FARGO 107 1 b3 a1 b3|a1 7 7 FARGO 106 1 b3 a3 b3|a3 8 8 FARGO 105 1 b3 a2 b3|a2 9 9 FARGO 109 1 b4 a0 b4|a0 10 10 FARGO 110 1 b4 a1 b4|a1"},{"path":"https://didiermurillof.github.io/FielDHub/articles/strip_plot.html","id":"access-to-strip-object","dir":"Articles","previous_headings":"2. Using the FielDHub function: strip_plot()","what":"Access to strip object","title":"Strip-Plot Design","text":"strip_plot() function returns list consisting information displayed output tabs FielDHub app: design information, plot layout, plot numbering, entries list, field book. accessible $ operator, .e.Β strip$layoutRandom strip$fieldBook. strip$fieldBook list containing information every plot field, information location plot treatment plot. seen output , field book columns ID, LOCATION, PLOT, REP, HSTRIP, VSTRIP, TRT_COMB.","code":"field_book <- strip$fieldBook head(strip$fieldBook, 10) ID LOCATION PLOT REP HSTRIP VSTRIP TRT_COMB 1 1 FARGO 101 1 b1 a0 b1|a0 2 2 FARGO 102 1 b1 a1 b1|a1 3 3 FARGO 103 1 b1 a3 b1|a3 4 4 FARGO 104 1 b1 a2 b1|a2 5 5 FARGO 108 1 b3 a0 b3|a0 6 6 FARGO 107 1 b3 a1 b3|a1 7 7 FARGO 106 1 b3 a3 b3|a3 8 8 FARGO 105 1 b3 a2 b3|a2 9 9 FARGO 109 1 b4 a0 b4|a0 10 10 FARGO 110 1 b4 a1 b4|a1"},{"path":"https://didiermurillof.github.io/FielDHub/articles/strip_plot.html","id":"plot-the-field-layout","dir":"Articles","previous_headings":"2. Using the FielDHub function: strip_plot()","what":"Plot the field layout","title":"Strip-Plot Design","text":"plotting layout function coordinates ROW COLUMN, can use generic function plot() follow,","code":"plot(strip)"},{"path":"https://didiermurillof.github.io/FielDHub/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Didier Murillo. Maintainer, author. Salvador Gezan. Author. Ana Heilman. Contributor. Thomas Walk. Contributor. Johan Aparicio. Contributor. Matthew Seefeldt. Contributor. Jean-Marc Montpetit. Contributor. Richard Horsley. Contributor. North Dakota State University. Copyright holder.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Murillo D, Gezan S (2024). FielDHub: Shiny App Design Experiments Life Sciences. R package version 1.4.2, https://didiermurillof.github.io/FielDHub/, https://github.com/DidierMurilloF/FielDHub.","code":"@Manual{, title = {FielDHub: A Shiny App for Design of Experiments in Life Sciences}, author = {Didier Murillo and Salvador Gezan}, year = {2024}, note = {R package version 1.4.2, https://didiermurillof.github.io/FielDHub/}, url = {https://github.com/DidierMurilloF/FielDHub}, }"},{"path":[]},{"path":"https://didiermurillof.github.io/FielDHub/index.html","id":"overview","dir":"","previous_headings":"","what":"Overview","title":"A Shiny App for Design of Experiments in Life Sciences","text":"FielDHub R package/shiny design experiments (DOE) app aids creation traditional, un-replicated, augmented partially-replicated designs applied agriculture, plant breeding, forestry, animal biological sciences. details examples functions present FielDHub package. Please, go https://didiermurillof.github.io/FielDHub/reference/index.html.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/index.html","id":"usage","dir":"","previous_headings":"","what":"Usage","title":"A Shiny App for Design of Experiments in Life Sciences","text":"basic example shows launch app:","code":"library(FielDHub) run_app()"},{"path":"https://didiermurillof.github.io/FielDHub/index.html","id":"diagonal-arrangement-example","dir":"","previous_headings":"Usage","what":"Diagonal Arrangement Example","title":"A Shiny App for Design of Experiments in Life Sciences","text":"project needs test 280 genotypes field containing 16 rows 20 columns plots. example, 280 genotypes divided among three different experiments. addition, four checks included systematic diagonal arrangement across experiments fill 40 plots representing 12.5% total number experimental plots. option include filler plots also available fields number experimental plots equal number available field plots. figure shows map experiment randomized along multiple experiments (three) checks diagonals. Distinctively colored check plots replicated throughout field systematic diagonal arrangement. figure shows layout three experiments field.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/index.html","id":"using-the-fieldhub-function-diagonal_arrangement","dir":"","previous_headings":"Usage","what":"Using the FielDHub function diagonal_arrangement()","title":"A Shiny App for Design of Experiments in Life Sciences","text":"illustrate using FielDHub build experimental designs R code, design produced R Shiny interface described can also created using function diagonal_arrangement() R script . Note, obtain identical results, users must include random seed script used Shiny app. case, random seed 1249. Users can print returned values diagonal_arrangement() follow, First 12 rows fieldbook, Users can plot layout design diagonal_arrangement() using function plot() follows, figure, salmon, green, blue shade blocks unreplicated experiments, distinctively colored check plots replicated throughout field systematic diagonal arrangement. main difference using FielDHub Shiny app using standalone function diagonal_arrangement() standalone function allocate filler necessary, Shiny App, users can customize number fillers needed. cases users include fillers, either experiments, Shiny app preferable filling visualizing field plots. see examples, go https://didiermurillof.github.io/FielDHub/articles/diagonal_arrangement.html","code":"diagonal <- diagonal_arrangement( nrows = 16, ncols = 20, lines = 280, checks = 4, plotNumber = 101, splitBy = \"row\", seed = 1249, kindExpt = \"DBUDC\", blocks = c(100, 100, 80), exptName = c(\"Expt1\", \"Expt2\", \"Expt3\") ) print(diagonal) Un-replicated Diagonal Arrangement Design Information on the design parameters: List of 11 $ rows : num 16 $ columns : num 20 $ treatments : num [1:3] 100 100 80 $ checks : int 4 $ entry_checks :List of 1 ..$ : int [1:4] 1 2 3 4 $ rep_checks :List of 1 ..$ : num [1:4] 10 10 10 10 $ locations : num 1 $ planter : chr \"serpentine\" $ percent_checks: chr \"12.5%\" $ fillers : num 0 $ seed : num 1249 10 First observations of the data frame with the diagonal_arrangement field book: ID EXPT LOCATION YEAR PLOT ROW COLUMN CHECKS ENTRY TREATMENT 1 1 Expt1 1 2023 101 1 1 0 42 Gen-42 2 2 Expt1 1 2023 102 1 2 0 23 Gen-23 3 3 Expt1 1 2023 103 1 3 0 10 Gen-10 4 4 Expt1 1 2023 104 1 4 0 45 Gen-45 5 5 Expt1 1 2023 105 1 5 0 51 Gen-51 6 6 Expt1 1 2023 106 1 6 0 13 Gen-13 7 7 Expt1 1 2023 107 1 7 3 3 Check-3 8 8 Expt1 1 2023 108 1 8 0 43 Gen-43 9 9 Expt1 1 2023 109 1 9 0 84 Gen-84 10 10 Expt1 1 2023 110 1 10 0 102 Gen-102 head(diagonal$fieldBook, 12) ID EXPT LOCATION YEAR PLOT ROW COLUMN CHECKS ENTRY TREATMENT 1 1 Expt1 1 2023 101 1 1 0 42 Gen-42 2 2 Expt1 1 2023 102 1 2 0 23 Gen-23 3 3 Expt1 1 2023 103 1 3 0 10 Gen-10 4 4 Expt1 1 2023 104 1 4 0 45 Gen-45 5 5 Expt1 1 2023 105 1 5 0 51 Gen-51 6 6 Expt1 1 2023 106 1 6 0 13 Gen-13 7 7 Expt1 1 2023 107 1 7 3 3 Check-3 8 8 Expt1 1 2023 108 1 8 0 43 Gen-43 9 9 Expt1 1 2023 109 1 9 0 84 Gen-84 10 10 Expt1 1 2023 110 1 10 0 102 Gen-102 11 11 Expt1 1 2023 111 1 11 0 89 Gen-89 12 12 Expt1 1 2023 112 1 12 0 75 Gen-75 plot(diagonal)"},{"path":"https://didiermurillof.github.io/FielDHub/index.html","id":"partially-replicated-design-example","dir":"","previous_headings":"Usage","what":"Partially Replicated Design Example","title":"A Shiny App for Design of Experiments in Life Sciences","text":"Partially replicated designs commonly employed early generation field trials. type design characterized replication portion entries, remaining entries appearing experiment. example, considered field trial 288 plots containing 75 entries appearing two times , 138 entries appearing . field trials arranged field 16 rows 18 columns. figure , green plots contain replicated entries, plots contain entries appear .","code":""},{"path":"https://didiermurillof.github.io/FielDHub/index.html","id":"using-the-fieldhub-function-partially_replicated","dir":"","previous_headings":"Usage","what":"Using the FielDHub function partially_replicated()","title":"A Shiny App for Design of Experiments in Life Sciences","text":"Instead using Shiny FielDHub app, users can use standalone FielDHub function partially_replicated(). partially replicated layout described can produced scripting follows. noted previous example, obtain identical results script Shiny app, users need use random seed, , case, 77. Users can print returned values partially_replicated() follows, First 12 rows fieldbook, Users can plot layout design partially_replicated() using function plot() follows, see examples, please go https://didiermurillof.github.io/FielDHub/articles/partially_replicated.html","code":"pREP <- partially_replicated( nrows = 16, ncols = 18, repGens = c(138,75), repUnits = c(1,2), planter = \"serpentine\", plotNumber = 1, exptName = \"ExptA\", locationNames = \"FARGO\", seed = 77 ) print(pREP) Partially Replicated Design Replications within location: LOCATION Replicated Unreplicated 1 FARGO 75 138 Information on the design parameters: List of 7 $ rows : num 16 $ columns : num 18 $ min_distance : num 8 $ incidence_in_rows: num 3 $ locations : num 1 $ planter : chr \"serpentine\" $ seed : num 77 10 First observations of the data frame with the partially_replicated field book: ID EXPT LOCATION YEAR PLOT ROW COLUMN CHECKS ENTRY TREATMENT 1 1 ExptA FARGO 2023 1 1 1 30 30 G30 2 2 ExptA FARGO 2023 2 1 2 0 192 G192 3 3 ExptA FARGO 2023 3 1 3 44 44 G44 4 4 ExptA FARGO 2023 4 1 4 66 66 G66 5 5 ExptA FARGO 2023 5 1 5 0 78 G78 6 6 ExptA FARGO 2023 6 1 6 0 186 G186 7 7 ExptA FARGO 2023 7 1 7 34 34 G34 8 8 ExptA FARGO 2023 8 1 8 0 86 G86 9 9 ExptA FARGO 2023 9 1 9 37 37 G37 10 10 ExptA FARGO 2023 10 1 10 55 55 G55 head(pREP$fieldBook, 12) ID EXPT LOCATION YEAR PLOT ROW COLUMN CHECKS ENTRY TREATMENT 1 1 ExptA FARGO 2023 1 1 1 30 30 G30 2 2 ExptA FARGO 2023 2 1 2 0 192 G192 3 3 ExptA FARGO 2023 3 1 3 44 44 G44 4 4 ExptA FARGO 2023 4 1 4 66 66 G66 5 5 ExptA FARGO 2023 5 1 5 0 78 G78 6 6 ExptA FARGO 2023 6 1 6 0 186 G186 7 7 ExptA FARGO 2023 7 1 7 34 34 G34 8 8 ExptA FARGO 2023 8 1 8 0 86 G86 9 9 ExptA FARGO 2023 9 1 9 37 37 G37 10 10 ExptA FARGO 2023 10 1 10 55 55 G55 11 11 ExptA FARGO 2023 11 1 11 0 125 G125 12 12 ExptA FARGO 2023 12 1 12 0 159 G159 plot(pREP)"},{"path":"https://didiermurillof.github.io/FielDHub/reference/CRD.html","id":null,"dir":"Reference","previous_headings":"","what":"Generates a Completely Randomized Design (CRD) β€” CRD","title":"Generates a Completely Randomized Design (CRD) β€” CRD","text":"randomly generates completely randomized design.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/CRD.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generates a Completely Randomized Design (CRD) β€” CRD","text":"","code":"CRD( t = NULL, reps = NULL, plotNumber = 101, locationName = NULL, seed = NULL, data = NULL )"},{"path":"https://didiermurillof.github.io/FielDHub/reference/CRD.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generates a Completely Randomized Design (CRD) β€” CRD","text":"t integer number total number treatments vector dimension t labels. reps Number replicates treatment. plotNumber Starting plot number. default plotNumber = 101. locationName (optional) Name location. seed (optional) Real number specifies starting seed obtain reproducible designs. data (optional) Data frame 2 columns labels treatments number replicates.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/CRD.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generates a Completely Randomized Design (CRD) β€” CRD","text":"list two elements. infoDesign list information design parameters. fieldBook data frame CRD field book.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/CRD.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Generates a Completely Randomized Design (CRD) β€” CRD","text":"Federer, W. T. (1955). Experimental Design. Theory Application. New York, USA. Macmillan Company.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/CRD.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Generates a Completely Randomized Design (CRD) β€” CRD","text":"Didier Murillo [aut], Salvador Gezan [aut], Ana Heilman [ctb], Thomas Walk [ctb], Johan Aparicio [ctb], Richard Horsley [ctb]","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/CRD.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generates a Completely Randomized Design (CRD) β€” CRD","text":"","code":"# Example 1: Generates a CRD design with 10 treatments and 5 reps each. crd1 <- CRD( t = 10, reps = 5, plotNumber = 101, seed = 1987, locationName = \"Fargo\" ) crd1$infoDesign #> $numberofTreatments #> [1] 10 #> #> $treatments #> [1] \"T1\" \"T2\" \"T3\" \"T4\" \"T5\" \"T6\" \"T7\" \"T8\" \"T9\" \"T10\" #> #> $Reps #> [1] 5 #> #> $locationName #> [1] \"Fargo\" #> #> $seed #> [1] 1987 #> #> $id_design #> [1] 1 #> head(crd1$fieldBook, 10) #> ID LOCATION PLOT REP TREATMENT #> 1 1 Fargo 101 1 T10 #> 2 2 Fargo 102 4 T1 #> 3 3 Fargo 103 3 T3 #> 4 4 Fargo 104 3 T9 #> 5 5 Fargo 105 4 T3 #> 6 6 Fargo 106 5 T3 #> 7 7 Fargo 107 1 T2 #> 8 8 Fargo 108 3 T4 #> 9 9 Fargo 109 1 T1 #> 10 10 Fargo 110 3 T1 # Example 2: Generates a CRD design with 15 treatments and 6 reps each. Gens <- paste(\"Wheat\", 1:15, sep = \"\") crd2 <- CRD( t = Gens, reps = 6, plotNumber = 1001, seed = 1654, locationName = \"Fargo\" ) crd2$infoDesign #> $numberofTreatments #> [1] 15 #> #> $treatments #> [1] \"Wheat1\" \"Wheat2\" \"Wheat3\" \"Wheat4\" \"Wheat5\" \"Wheat6\" \"Wheat7\" #> [8] \"Wheat8\" \"Wheat9\" \"Wheat10\" \"Wheat11\" \"Wheat12\" \"Wheat13\" \"Wheat14\" #> [15] \"Wheat15\" #> #> $Reps #> [1] 6 #> #> $locationName #> [1] \"Fargo\" #> #> $seed #> [1] 1654 #> #> $id_design #> [1] 1 #> head(crd2$fieldBook, 10) #> ID LOCATION PLOT REP TREATMENT #> 1 1 Fargo 1001 6 Wheat3 #> 2 2 Fargo 1002 2 Wheat8 #> 3 3 Fargo 1003 2 Wheat2 #> 4 4 Fargo 1004 4 Wheat4 #> 5 5 Fargo 1005 1 Wheat1 #> 6 6 Fargo 1006 1 Wheat4 #> 7 7 Fargo 1007 1 Wheat13 #> 8 8 Fargo 1008 1 Wheat1 #> 9 9 Fargo 1009 6 Wheat15 #> 10 10 Fargo 1010 4 Wheat7 # Example 3: Generates a CRD design with 12 treatments and 4 reps each. # In this case, we show how to use the option data. treatments <- paste(\"ND-\", 1:12, sep = \"\") treatment_list <- data.frame(list(TREATMENT = treatments, REP = 4)) head(treatment_list) #> TREATMENT REP #> 1 ND-1 4 #> 2 ND-2 4 #> 3 ND-3 4 #> 4 ND-4 4 #> 5 ND-5 4 #> 6 ND-6 4 crd3 <- CRD( t = NULL, reps = NULL, plotNumber = 2001, seed = 1655, locationName = \"Cali\", data = treatment_list ) crd3$infoDesign #> $numberofTreatments #> [1] 12 #> #> $treatments #> [1] \"ND-1\" \"ND-2\" \"ND-3\" \"ND-4\" \"ND-5\" \"ND-6\" \"ND-7\" \"ND-8\" \"ND-9\" #> [10] \"ND-10\" \"ND-11\" \"ND-12\" #> #> $Reps #> [1] 4 4 4 4 4 4 4 4 4 4 4 4 #> #> $locationName #> [1] \"Cali\" #> #> $seed #> [1] 1655 #> #> $id_design #> [1] 1 #> head(crd3$fieldBook, 10) #> ID LOCATION PLOT REP TREATMENT #> 1 1 Cali 2001 4 ND-3 #> 2 2 Cali 2002 1 ND-7 #> 3 3 Cali 2003 2 ND-2 #> 4 4 Cali 2004 3 ND-8 #> 5 5 Cali 2005 3 ND-2 #> 6 6 Cali 2006 1 ND-10 #> 7 7 Cali 2007 3 ND-7 #> 8 8 Cali 2008 4 ND-9 #> 9 9 Cali 2009 3 ND-4 #> 10 10 Cali 2010 1 ND-3"},{"path":"https://didiermurillof.github.io/FielDHub/reference/RCBD.html","id":null,"dir":"Reference","previous_headings":"","what":"Generates a Randomized Complete Block Design (RCBD) β€” RCBD","title":"Generates a Randomized Complete Block Design (RCBD) β€” RCBD","text":"randomly generates randomized complete block design (RCBD) across locations.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/RCBD.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generates a Randomized Complete Block Design (RCBD) β€” RCBD","text":"","code":"RCBD( t = NULL, reps = NULL, l = 1, plotNumber = 101, continuous = FALSE, planter = \"serpentine\", seed = NULL, locationNames = NULL, data = NULL )"},{"path":"https://didiermurillof.github.io/FielDHub/reference/RCBD.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generates a Randomized Complete Block Design (RCBD) β€” RCBD","text":"t integer number total number treatments vector dimension t labels. reps Number replicates (full blocks) treatment. l Number locations. default l = 1. plotNumber Numeric vector starting plot number location. default plotNumber = 101. continuous Logical value plot number continuous . default continuous = FALSE. planter Option serpentine cartesian arrangement. default planter = 'serpentine'. seed (optional) Real number specifies starting seed obtain reproducible designs. locationNames (optional) Names location. data (optional) Data frame labels treatments.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/RCBD.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generates a Randomized Complete Block Design (RCBD) β€” RCBD","text":"list five elements. infoDesign list information design parameters. layoutRandom RCBD layout randomization location. plotNumber plot number layout location. fieldBook data frame RCBD field book design.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/RCBD.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Generates a Randomized Complete Block Design (RCBD) β€” RCBD","text":"Federer, W. T. (1955). Experimental Design. Theory Application. New York, USA. Macmillan Company.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/RCBD.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Generates a Randomized Complete Block Design (RCBD) β€” RCBD","text":"Didier Murillo [aut], Salvador Gezan [aut], Ana Heilman [ctb], Thomas Walk [ctb], Johan Aparicio [ctb], Richard Horsley [ctb]","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/RCBD.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generates a Randomized Complete Block Design (RCBD) β€” RCBD","text":"","code":"# Example 1: Generates a RCBD design with 3 blocks and 20 treatments across 3 locations. rcbd1 <- RCBD(t = LETTERS[1:20], reps = 5, l = 3, plotNumber = c(101,1001, 2001), continuous = TRUE, planter = \"serpentine\", seed = 1020, locationNames = c(\"FARGO\", \"MINOT\", \"CASSELTON\")) rcbd1$infoDesign #> $blocks #> [1] 5 #> #> $number.of.treatments #> [1] 20 #> #> $treatments #> [1] \"A\" \"B\" \"C\" \"D\" \"E\" \"F\" \"G\" \"H\" \"I\" \"J\" \"K\" \"L\" \"M\" \"N\" \"O\" \"P\" \"Q\" \"R\" \"S\" #> [20] \"T\" #> #> $locations #> [1] 3 #> #> $plotNumber #> [1] 101 201 301 401 501 1001 1101 1201 1301 1401 2001 2101 2201 2301 2401 #> #> $locationNames #> [1] \"FARGO\" \"MINOT\" \"CASSELTON\" #> #> $seed #> [1] 1020 #> #> $id_design #> [1] 2 #> rcbd1$layoutRandom #> $Loc_FARGO #> Block --Treatments-- #> [1,] \"1\" \"P R L T E A J O M C K F I Q G D S H N B\" #> [2,] \"2\" \"Q H G M F D L P E B J N A I K C T R O S\" #> [3,] \"3\" \"R B G K H E S C F D I T P N Q M A O J L\" #> [4,] \"4\" \"M I T B N G O J Q C A L P E S R D K H F\" #> [5,] \"5\" \"M C Q O E H I A P S R L J G F B T D K N\" #> #> $Loc_MINOT #> Block --Treatments-- #> [1,] \"1\" \"F O C A G D L B I S P T H K M E N R Q J\" #> [2,] \"2\" \"Q H K A G D E M N O C S J I T L P F B R\" #> [3,] \"3\" \"B K D L O E A R F S I P G T C Q J N M H\" #> [4,] \"4\" \"C P L O B K E H Q G N A T R J F S M D I\" #> [5,] \"5\" \"G S D B H L Q K A P E J T R I C O F M N\" #> #> $Loc_CASSELTON #> Block --Treatments-- #> [1,] \"1\" \"P G T E L O K H D N S C M I A J Q R B F\" #> [2,] \"2\" \"C D L F A T I G S O B J M E R P H N Q K\" #> [3,] \"3\" \"C G K N B A L Q I F D H J M O P S T E R\" #> [4,] \"4\" \"E L H D F J A T S N B G Q M I O P C K R\" #> [5,] \"5\" \"T I M A H K E C Q L D J R B G S N O F P\" #> rcbd1$plotNumber #> $Loc_FARGO #> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14] #> [1,] 101 102 103 104 105 106 107 108 109 110 111 112 113 114 #> [2,] 140 139 138 137 136 135 134 133 132 131 130 129 128 127 #> [3,] 141 142 143 144 145 146 147 148 149 150 151 152 153 154 #> [4,] 180 179 178 177 176 175 174 173 172 171 170 169 168 167 #> [5,] 181 182 183 184 185 186 187 188 189 190 191 192 193 194 #> [,15] [,16] [,17] [,18] [,19] [,20] #> [1,] 115 116 117 118 119 120 #> [2,] 126 125 124 123 122 121 #> [3,] 155 156 157 158 159 160 #> [4,] 166 165 164 163 162 161 #> [5,] 195 196 197 198 199 200 #> #> $Loc_MINOT #> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14] #> [1,] 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 #> [2,] 1040 1039 1038 1037 1036 1035 1034 1033 1032 1031 1030 1029 1028 1027 #> [3,] 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 #> [4,] 1080 1079 1078 1077 1076 1075 1074 1073 1072 1071 1070 1069 1068 1067 #> [5,] 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 #> [,15] [,16] [,17] [,18] [,19] [,20] #> [1,] 1015 1016 1017 1018 1019 1020 #> [2,] 1026 1025 1024 1023 1022 1021 #> [3,] 1055 1056 1057 1058 1059 1060 #> [4,] 1066 1065 1064 1063 1062 1061 #> [5,] 1095 1096 1097 1098 1099 1100 #> #> $Loc_CASSELTON #> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14] #> [1,] 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 #> [2,] 2040 2039 2038 2037 2036 2035 2034 2033 2032 2031 2030 2029 2028 2027 #> [3,] 2041 2042 2043 2044 2045 2046 2047 2048 2049 2050 2051 2052 2053 2054 #> [4,] 2080 2079 2078 2077 2076 2075 2074 2073 2072 2071 2070 2069 2068 2067 #> [5,] 2081 2082 2083 2084 2085 2086 2087 2088 2089 2090 2091 2092 2093 2094 #> [,15] [,16] [,17] [,18] [,19] [,20] #> [1,] 2015 2016 2017 2018 2019 2020 #> [2,] 2026 2025 2024 2023 2022 2021 #> [3,] 2055 2056 2057 2058 2059 2060 #> [4,] 2066 2065 2064 2063 2062 2061 #> [5,] 2095 2096 2097 2098 2099 2100 #> head(rcbd1$fieldBook) #> ID LOCATION PLOT REP TREATMENT #> 1 1 FARGO 101 1 P #> 2 2 FARGO 102 1 R #> 3 3 FARGO 103 1 L #> 4 4 FARGO 104 1 T #> 5 5 FARGO 105 1 E #> 6 6 FARGO 106 1 A # Example 2: Generates a RCBD design with 6 blocks and 18 treatments in one location. # In this case, we show how to use the option data. treatments <- paste(\"ND-\", 1:18, sep = \"\") treatment_list <- data.frame(list(TREATMENT = treatments)) head(treatment_list) #> TREATMENT #> 1 ND-1 #> 2 ND-2 #> 3 ND-3 #> 4 ND-4 #> 5 ND-5 #> 6 ND-6 rcbd2 <- RCBD(reps = 6, l = 1, plotNumber = 101, continuous = FALSE, planter = \"serpentine\", seed = 13, locationNames = \"IBAGUE\", data = treatment_list) rcbd2$infoDesign #> $blocks #> [1] 6 #> #> $number.of.treatments #> [1] 18 #> #> $treatments #> [1] \"ND-1\" \"ND-2\" \"ND-3\" \"ND-4\" \"ND-5\" \"ND-6\" \"ND-7\" \"ND-8\" \"ND-9\" #> [10] \"ND-10\" \"ND-11\" \"ND-12\" \"ND-13\" \"ND-14\" \"ND-15\" \"ND-16\" \"ND-17\" \"ND-18\" #> #> $locations #> [1] 1 #> #> $plotNumber #> [1] 101 201 301 401 501 601 #> #> $locationNames #> [1] \"IBAGUE\" #> #> $seed #> [1] 13 #> #> $id_design #> [1] 2 #> rcbd2$layoutRandom #> $Loc_IBAGUE #> Block #> [1,] \"1\" #> [2,] \"2\" #> [3,] \"3\" #> [4,] \"4\" #> [5,] \"5\" #> [6,] \"6\" #> --Treatments-- #> [1,] \"ND-3 ND-5 ND-10 ND-13 ND-6 ND-14 ND-4 ND-8 ND-18 ND-1 ND-11 ND-2 ND-17 ND-12 ND-9 ND-7 ND-16 ND-15\" #> [2,] \"ND-15 ND-17 ND-12 ND-1 ND-11 ND-4 ND-8 ND-7 ND-5 ND-3 ND-14 ND-9 ND-10 ND-13 ND-2 ND-6 ND-18 ND-16\" #> [3,] \"ND-17 ND-12 ND-8 ND-14 ND-10 ND-6 ND-7 ND-18 ND-2 ND-1 ND-13 ND-9 ND-11 ND-15 ND-16 ND-3 ND-4 ND-5\" #> [4,] \"ND-14 ND-13 ND-16 ND-1 ND-8 ND-9 ND-15 ND-6 ND-7 ND-12 ND-10 ND-18 ND-11 ND-4 ND-3 ND-5 ND-2 ND-17\" #> [5,] \"ND-14 ND-11 ND-9 ND-4 ND-1 ND-16 ND-3 ND-8 ND-5 ND-7 ND-10 ND-18 ND-12 ND-6 ND-2 ND-15 ND-13 ND-17\" #> [6,] \"ND-3 ND-5 ND-17 ND-9 ND-6 ND-18 ND-1 ND-14 ND-12 ND-8 ND-4 ND-11 ND-15 ND-2 ND-10 ND-16 ND-13 ND-7\" #> rcbd2$plotNumber #> $Loc_IBAGUE #> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14] #> [1,] 101 102 103 104 105 106 107 108 109 110 111 112 113 114 #> [2,] 218 217 216 215 214 213 212 211 210 209 208 207 206 205 #> [3,] 301 302 303 304 305 306 307 308 309 310 311 312 313 314 #> [4,] 418 417 416 415 414 413 412 411 410 409 408 407 406 405 #> [5,] 501 502 503 504 505 506 507 508 509 510 511 512 513 514 #> [6,] 618 617 616 615 614 613 612 611 610 609 608 607 606 605 #> [,15] [,16] [,17] [,18] #> [1,] 115 116 117 118 #> [2,] 204 203 202 201 #> [3,] 315 316 317 318 #> [4,] 404 403 402 401 #> [5,] 515 516 517 518 #> [6,] 604 603 602 601 #> head(rcbd2$fieldBook) #> ID LOCATION PLOT REP TREATMENT #> 1 1 IBAGUE 101 1 ND-3 #> 2 2 IBAGUE 102 1 ND-5 #> 3 3 IBAGUE 103 1 ND-10 #> 4 4 IBAGUE 104 1 ND-13 #> 5 5 IBAGUE 105 1 ND-6 #> 6 6 IBAGUE 106 1 ND-14"},{"path":"https://didiermurillof.github.io/FielDHub/reference/RCBD_augmented.html","id":null,"dir":"Reference","previous_headings":"","what":"Generates an Augmented Randomized Complete Block Design (ARCBD) β€” RCBD_augmented","title":"Generates an Augmented Randomized Complete Block Design (ARCBD) β€” RCBD_augmented","text":"randomly generates augmented randomized complete block design across locations (ARCBD).","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/RCBD_augmented.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generates an Augmented Randomized Complete Block Design (ARCBD) β€” RCBD_augmented","text":"","code":"RCBD_augmented( lines = NULL, checks = NULL, b = NULL, l = 1, planter = \"serpentine\", plotNumber = 101, exptName = NULL, seed = NULL, locationNames = NULL, repsExpt = 1, random = TRUE, data = NULL, nrows = NULL, ncols = NULL )"},{"path":"https://didiermurillof.github.io/FielDHub/reference/RCBD_augmented.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generates an Augmented Randomized Complete Block Design (ARCBD) β€” RCBD_augmented","text":"lines Treatments, number lines test. checks Number checks per augmented block. b Number augmented blocks. l Number locations. default l = 1. planter Option serpentine cartesian arrangement. default planter = 'serpentine'. plotNumber Numeric vector starting plot number location. default plotNumber = 101. exptName (optional) Name experiment. seed (optional) Real number specifies starting seed obtain reproducible designs. locationNames (optional) Name location. repsExpt (optional) Number reps experiment. default repsExpt = 1. random Logical value randomize treatments . default random = TRUE. data (optional) Data frame labels treatments. nrows (optional) Number rows field. ncols (optional) Number columns field.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/RCBD_augmented.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generates an Augmented Randomized Complete Block Design (ARCBD) β€” RCBD_augmented","text":"list five elements. infoDesign list information design parameters. layoutRandom ARCBD layout randomization first location. plotNumber plot number layout first location. exptNames experiment names layout. data_entry data frame data input. fieldBook data frame ARCBD field book.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/RCBD_augmented.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Generates an Augmented Randomized Complete Block Design (ARCBD) β€” RCBD_augmented","text":"Federer, W. T. (1955). Experimental Design. Theory Application. New York, USA. Macmillan Company.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/RCBD_augmented.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Generates an Augmented Randomized Complete Block Design (ARCBD) β€” RCBD_augmented","text":"Didier Murillo [aut], Salvador Gezan [aut], Ana Heilman [ctb], Thomas Walk [ctb], Johan Aparicio [ctb], Richard Horsley [ctb]","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/RCBD_augmented.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generates an Augmented Randomized Complete Block Design (ARCBD) β€” RCBD_augmented","text":"","code":"# Example 1: Generates an ARCBD with 6 blocks, 3 checks for each, and 50 treatments # in two locations. ARCBD1 <- RCBD_augmented(lines = 50, checks = 3, b = 6, l = 2, planter = \"cartesian\", plotNumber = c(1,1001), seed = 23, locationNames = c(\"FARGO\", \"MINOT\")) ARCBD1$infoDesign #> $rows #> [1] 6 #> #> $columns #> [1] 12 #> #> $rows_within_blocks #> [1] 1 #> #> $columns_within_blocks #> [1] 12 #> #> $treatments #> [1] 50 #> #> $checks #> [1] 3 #> #> $blocks #> [1] 6 #> #> $plots_per_block #> [1] 12 12 12 12 12 8 #> #> $locations #> [1] 2 #> #> $fillers #> [1] 4 #> #> $seed #> [1] 23 #> #> $id_design #> [1] 14 #> ARCBD1$layoutRandom #> Col1 Col2 Col3 Col4 Col5 Col6 Col7 Col8 Col9 Col10 Col11 Col12 #> Row6 2 15 38 3 21 36 26 1 0 0 0 0 #> Row5 3 1 24 46 11 2 48 37 32 31 20 42 #> Row4 34 25 16 41 9 50 2 43 39 1 13 3 #> Row3 18 28 5 2 40 8 30 17 53 10 3 1 #> Row2 7 29 12 2 3 33 22 23 4 47 19 1 #> Row1 49 14 27 3 2 45 6 35 52 44 51 1 ARCBD1$exptNames #> V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 #> 1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 #> 2 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 #> 3 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 #> 4 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 #> 5 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 #> 6 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 ARCBD1$plotNumber #> V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 #> [1,] 61 62 63 64 65 66 67 68 0 0 0 0 #> [2,] 49 50 51 52 53 54 55 56 57 58 59 60 #> [3,] 37 38 39 40 41 42 43 44 45 46 47 48 #> [4,] 25 26 27 28 29 30 31 32 33 34 35 36 #> [5,] 13 14 15 16 17 18 19 20 21 22 23 24 #> [6,] 1 2 3 4 5 6 7 8 9 10 11 12 head(ARCBD1$fieldBook, 12) #> ID EXPT LOCATION YEAR PLOT ROW COLUMN CHECKS BLOCK ENTRY TREATMENT #> 1 1 Expt1 FARGO 2024 1 1 1 0 1 49 G49 #> 2 2 Expt1 FARGO 2024 2 1 2 0 1 14 G14 #> 3 3 Expt1 FARGO 2024 3 1 3 0 1 27 G27 #> 4 4 Expt1 FARGO 2024 4 1 4 1 1 3 CH3 #> 5 5 Expt1 FARGO 2024 5 1 5 1 1 2 CH2 #> 6 6 Expt1 FARGO 2024 6 1 6 0 1 45 G45 #> 7 7 Expt1 FARGO 2024 7 1 7 0 1 6 G6 #> 8 8 Expt1 FARGO 2024 8 1 8 0 1 35 G35 #> 9 9 Expt1 FARGO 2024 9 1 9 0 1 52 G52 #> 10 10 Expt1 FARGO 2024 10 1 10 0 1 44 G44 #> 11 11 Expt1 FARGO 2024 11 1 11 0 1 51 G51 #> 12 12 Expt1 FARGO 2024 12 1 12 1 1 1 CH1 # Example 2: Generates an ARCBD with 17 blocks, 4 checks for each, and 350 treatments # in 3 locations. # In this case, we show how to use the option data. checks <- 4; list_checks <- paste(\"CH\", 1:checks, sep = \"\") treatments <- paste(\"G\", 5:354, sep = \"\") treatment_list <- data.frame(list(ENTRY = 1:354, NAME = c(list_checks, treatments))) head(treatment_list, 12) #> ENTRY NAME #> 1 1 CH1 #> 2 2 CH2 #> 3 3 CH3 #> 4 4 CH4 #> 5 5 G5 #> 6 6 G6 #> 7 7 G7 #> 8 8 G8 #> 9 9 G9 #> 10 10 G10 #> 11 11 G11 #> 12 12 G12 ARCBD2 <- RCBD_augmented(lines = 350, checks = 4, b = 17, l = 3, planter = \"serpentine\", plotNumber = c(101,1001,2001), seed = 24, locationNames = LETTERS[1:3], data = treatment_list) ARCBD2$infoDesign #> $rows #> [1] 17 #> #> $columns #> [1] 25 #> #> $rows_within_blocks #> [1] 1 #> #> $columns_within_blocks #> [1] 25 #> #> $treatments #> [1] 350 #> #> $checks #> [1] 4 #> #> $blocks #> [1] 17 #> #> $plots_per_block #> [1] 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 18 #> #> $locations #> [1] 3 #> #> $fillers #> [1] 7 #> #> $seed #> [1] 24 #> #> $id_design #> [1] 14 #> ARCBD2$layoutRandom #> Col1 Col2 Col3 Col4 Col5 Col6 Col7 Col8 Col9 Col10 Col11 Col12 Col13 #> Row17 257 259 1 198 331 66 3 238 170 176 126 207 225 #> Row16 17 12 1 314 22 235 77 340 188 76 101 2 16 #> Row15 229 231 54 3 305 4 128 50 30 55 1 337 24 #> Row14 63 45 62 40 140 322 82 228 283 142 53 211 7 #> Row13 253 2 68 113 13 279 47 57 4 132 3 167 159 #> Row12 282 205 192 324 315 2 247 124 179 58 105 273 31 #> Row11 110 125 85 332 250 248 265 255 2 251 52 42 236 #> Row10 173 154 338 327 78 3 96 177 193 4 244 191 348 #> Row9 4 2 25 103 36 155 260 246 189 49 197 284 242 #> Row8 107 321 186 4 163 33 71 109 100 174 309 18 135 #> Row7 2 239 252 213 261 150 3 266 277 307 4 95 311 #> Row6 133 75 153 102 274 2 4 1 270 285 3 240 276 #> Row5 234 56 349 288 202 300 79 87 157 64 168 1 4 #> Row4 160 195 2 289 161 83 143 271 141 144 94 320 3 #> Row3 268 209 4 185 308 115 81 342 249 258 120 1 2 #> Row2 172 347 346 215 298 86 1 116 328 224 139 3 4 #> Row1 345 130 4 162 1 123 2 39 9 302 210 352 138 #> Col14 Col15 Col16 Col17 Col18 Col19 Col20 Col21 Col22 Col23 Col24 Col25 #> Row17 122 4 208 187 2 0 0 0 0 0 0 0 #> Row16 3 219 111 291 316 4 341 169 222 237 65 281 #> Row15 329 263 2 74 108 318 350 147 306 325 37 43 #> Row14 136 310 5 2 199 1 4 164 46 3 158 223 #> Row13 23 1 148 117 201 28 11 119 190 73 72 99 #> Row12 32 1 27 243 241 21 3 303 4 106 127 254 #> Row11 35 1 216 61 3 4 230 69 245 339 98 14 #> Row10 1 227 323 2 203 118 181 88 104 10 272 175 #> Row9 335 217 319 200 3 152 97 267 44 275 92 1 #> Row8 221 333 121 2 1 3 214 226 183 15 194 351 #> Row7 313 60 293 38 59 67 232 134 1 178 93 114 #> Row6 156 41 165 146 51 317 292 280 343 171 334 84 #> Row5 220 34 131 262 3 180 129 145 2 212 91 278 #> Row4 19 353 301 6 4 206 304 1 233 354 166 20 #> Row3 294 89 269 29 26 286 290 336 80 3 149 312 #> Row2 48 295 151 287 2 326 70 264 204 137 296 8 #> Row1 297 330 3 256 90 184 196 218 344 299 182 112 ARCBD2$exptNames #> V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 #> 1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 #> 2 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 #> 3 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 #> 4 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 #> 5 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 #> 6 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 #> 7 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 #> 8 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 #> 9 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 #> 10 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 #> 11 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 #> 12 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 #> 13 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 #> 14 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 #> 15 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 #> 16 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 #> 17 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 #> V13 V14 V15 V16 V17 V18 V19 V20 V21 V22 V23 V24 #> 1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 #> 2 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 #> 3 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 #> 4 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 #> 5 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 #> 6 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 #> 7 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 #> 8 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 #> 9 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 #> 10 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 #> 11 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 #> 12 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 #> 13 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 #> 14 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 #> 15 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 #> 16 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 #> 17 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 #> V25 #> 1 Expt1 #> 2 Expt1 #> 3 Expt1 #> 4 Expt1 #> 5 Expt1 #> 6 Expt1 #> 7 Expt1 #> 8 Expt1 #> 9 Expt1 #> 10 Expt1 #> 11 Expt1 #> 12 Expt1 #> 13 Expt1 #> 14 Expt1 #> 15 Expt1 #> 16 Expt1 #> 17 Expt1 ARCBD2$plotNumber #> V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 V13 V14 V15 V16 V17 V18 #> [1,] 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 #> [2,] 500 499 498 497 496 495 494 493 492 491 490 489 488 487 486 485 484 483 #> [3,] 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 #> [4,] 450 449 448 447 446 445 444 443 442 441 440 439 438 437 436 435 434 433 #> [5,] 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 #> [6,] 400 399 398 397 396 395 394 393 392 391 390 389 388 387 386 385 384 383 #> [7,] 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 #> [8,] 350 349 348 347 346 345 344 343 342 341 340 339 338 337 336 335 334 333 #> [9,] 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 #> [10,] 300 299 298 297 296 295 294 293 292 291 290 289 288 287 286 285 284 283 #> [11,] 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 #> [12,] 250 249 248 247 246 245 244 243 242 241 240 239 238 237 236 235 234 233 #> [13,] 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 #> [14,] 200 199 198 197 196 195 194 193 192 191 190 189 188 187 186 185 184 183 #> [15,] 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 #> [16,] 150 149 148 147 146 145 144 143 142 141 140 139 138 137 136 135 134 133 #> [17,] 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 #> V19 V20 V21 V22 V23 V24 V25 #> [1,] 0 0 0 0 0 0 0 #> [2,] 482 481 480 479 478 477 476 #> [3,] 469 470 471 472 473 474 475 #> [4,] 432 431 430 429 428 427 426 #> [5,] 419 420 421 422 423 424 425 #> [6,] 382 381 380 379 378 377 376 #> [7,] 369 370 371 372 373 374 375 #> [8,] 332 331 330 329 328 327 326 #> [9,] 319 320 321 322 323 324 325 #> [10,] 282 281 280 279 278 277 276 #> [11,] 269 270 271 272 273 274 275 #> [12,] 232 231 230 229 228 227 226 #> [13,] 219 220 221 222 223 224 225 #> [14,] 182 181 180 179 178 177 176 #> [15,] 169 170 171 172 173 174 175 #> [16,] 132 131 130 129 128 127 126 #> [17,] 119 120 121 122 123 124 125 head(ARCBD2$fieldBook, 12) #> ID EXPT LOCATION YEAR PLOT ROW COLUMN CHECKS BLOCK ENTRY TREATMENT #> 1 1 Expt1 A 2024 101 1 1 0 1 345 G345 #> 2 2 Expt1 A 2024 102 1 2 0 1 130 G130 #> 3 3 Expt1 A 2024 103 1 3 1 1 4 CH4 #> 4 4 Expt1 A 2024 104 1 4 0 1 162 G162 #> 5 5 Expt1 A 2024 105 1 5 1 1 1 CH1 #> 6 6 Expt1 A 2024 106 1 6 0 1 123 G123 #> 7 7 Expt1 A 2024 107 1 7 1 1 2 CH2 #> 8 8 Expt1 A 2024 108 1 8 0 1 39 G39 #> 9 9 Expt1 A 2024 109 1 9 0 1 9 G9 #> 10 10 Expt1 A 2024 110 1 10 0 1 302 G302 #> 11 11 Expt1 A 2024 111 1 11 0 1 210 G210 #> 12 12 Expt1 A 2024 112 1 12 0 1 352 G352"},{"path":"https://didiermurillof.github.io/FielDHub/reference/alpha_lattice.html","id":null,"dir":"Reference","previous_headings":"","what":"Generates an Alpha Design β€” alpha_lattice","title":"Generates an Alpha Design β€” alpha_lattice","text":"Randomly generates alpha design like alpha(0,1) across multiple locations.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/alpha_lattice.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generates an Alpha Design β€” alpha_lattice","text":"","code":"alpha_lattice( t = NULL, k = NULL, r = NULL, l = 1, plotNumber = 101, locationNames = NULL, seed = NULL, data = NULL )"},{"path":"https://didiermurillof.github.io/FielDHub/reference/alpha_lattice.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generates an Alpha Design β€” alpha_lattice","text":"t Number treatments. k Size incomplete blocks (number units per incomplete block). r Number full blocks (resolvable replicates) (also number replicates per treatment). l Number locations. default l = 1. plotNumber Numeric vector starting plot number location. default plotNumber = 101. locationNames (optional) String names l locations. seed (optional) Real number specifies starting seed obtain reproducible designs. data (optional) Data frame label list treatments.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/alpha_lattice.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generates an Alpha Design β€” alpha_lattice","text":"list two elements. infoDesign list information design parameters. fieldBook data frame alpha design field book.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/alpha_lattice.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Generates an Alpha Design β€” alpha_lattice","text":"Edmondson., R. N. (2021). blocksdesign: Nested crossed block designs factorial unstructured treatment sets. https://CRAN.R-project.org/package=blocksdesign","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/alpha_lattice.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Generates an Alpha Design β€” alpha_lattice","text":"Didier Murillo [aut], Salvador Gezan [aut], Ana Heilman [ctb], Thomas Walk [ctb], Johan Aparicio [ctb], Richard Horsley [ctb]","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/alpha_lattice.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generates an Alpha Design β€” alpha_lattice","text":"","code":"# Example 1: Generates an alpha design with 4 full blocks and 15 treatments. # Size of IBlocks k = 3. alphalattice1 <- alpha_lattice(t = 15, k = 3, r = 4, l = 1, plotNumber = 101, locationNames = \"GreenHouse\", seed = 1247) alphalattice1$infoDesign #> $Reps #> [1] 4 #> #> $iBlocks #> [1] 5 #> #> $NumberTreatments #> [1] 15 #> #> $NumberLocations #> [1] 1 #> #> $Locations #> [1] \"GREENHOUSE\" #> #> $seed #> [1] 1247 #> #> $lambda #> [1] 0.5714286 #> #> $id_design #> [1] 12 #> head(alphalattice1$fieldBook, 10) #> ID LOCATION PLOT REP IBLOCK UNIT ENTRY TREATMENT #> 1 1 GREENHOUSE 101 1 1 1 8 G-8 #> 2 2 GREENHOUSE 102 1 1 2 3 G-3 #> 3 3 GREENHOUSE 103 1 1 3 2 G-2 #> 4 4 GREENHOUSE 104 1 2 1 6 G-6 #> 5 5 GREENHOUSE 105 1 2 2 9 G-9 #> 6 6 GREENHOUSE 106 1 2 3 12 G-12 #> 7 7 GREENHOUSE 107 1 3 1 14 G-14 #> 8 8 GREENHOUSE 108 1 3 2 1 G-1 #> 9 9 GREENHOUSE 109 1 3 3 5 G-5 #> 10 10 GREENHOUSE 110 1 4 1 15 G-15 # Example 2: Generates an alpha design with 3 full blocks and 25 treatment. # Size of IBlocks k = 5. # In this case, we show how to use the option data. treatments <- paste(\"G-\", 1:25, sep = \"\") ENTRY <- 1:25 treatment_list <- data.frame(list(ENTRY = ENTRY, TREATMENT = treatments)) head(treatment_list) #> ENTRY TREATMENT #> 1 1 G-1 #> 2 2 G-2 #> 3 3 G-3 #> 4 4 G-4 #> 5 5 G-5 #> 6 6 G-6 alphalattice2 <- alpha_lattice(t = 25, k = 5, r = 3, l = 1, plotNumber = 1001, locationNames = \"A\", seed = 1945, data = treatment_list) alphalattice2$infoDesign #> $Reps #> [1] 3 #> #> $iBlocks #> [1] 5 #> #> $NumberTreatments #> [1] 25 #> #> $NumberLocations #> [1] 1 #> #> $Locations #> [1] \"A\" #> #> $seed #> [1] 1945 #> #> $lambda #> [1] 0.5 #> #> $id_design #> [1] 12 #> head(alphalattice2$fieldBook, 10) #> ID LOCATION PLOT REP IBLOCK UNIT ENTRY TREATMENT #> 1 1 A 1001 1 1 1 20 G-20 #> 2 2 A 1002 1 1 2 5 G-5 #> 3 3 A 1003 1 1 3 10 G-10 #> 4 4 A 1004 1 1 4 1 G-1 #> 5 5 A 1005 1 1 5 12 G-12 #> 6 6 A 1006 1 2 1 19 G-19 #> 7 7 A 1007 1 2 2 8 G-8 #> 8 8 A 1008 1 2 3 13 G-13 #> 9 9 A 1009 1 2 4 9 G-9 #> 10 10 A 1010 1 2 5 17 G-17"},{"path":"https://didiermurillof.github.io/FielDHub/reference/diagonal_arrangement.html","id":null,"dir":"Reference","previous_headings":"","what":"Spatial Un-replicated Diagonal Arrangement Design β€” diagonal_arrangement","title":"Spatial Un-replicated Diagonal Arrangement Design β€” diagonal_arrangement","text":"Randomly generates spatial un-replicated diagonal arrangement design.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/diagonal_arrangement.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Spatial Un-replicated Diagonal Arrangement Design β€” diagonal_arrangement","text":"","code":"diagonal_arrangement( nrows = NULL, ncols = NULL, lines = NULL, checks = NULL, planter = \"serpentine\", l = 1, plotNumber = 101, kindExpt = \"SUDC\", splitBy = \"row\", seed = NULL, blocks = NULL, exptName = NULL, locationNames = NULL, multiLocationData = FALSE, data = NULL )"},{"path":"https://didiermurillof.github.io/FielDHub/reference/diagonal_arrangement.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Spatial Un-replicated Diagonal Arrangement Design β€” diagonal_arrangement","text":"nrows Number rows field. ncols Number columns field. lines Number genotypes, experimental lines treatments. checks Number genotypes checks. planter Option serpentine cartesian plot arrangement. default planter = 'serpentine'. l Number locations sites. default l = 1. plotNumber Numeric vector starting plot number location. default plotNumber = 101. kindExpt Type diagonal design, single options: Single Un-replicated Diagonal Checks 'SUDC' Decision Blocks Un-replicated Design Diagonal Checks 'DBUDC' multiple experiments. default kindExpt = 'SUDC'. splitBy Option split field kindExpt = 'DBUDC' selected. default splitBy = 'row'. seed (optional) Real number specifies starting seed obtain reproducible designs. blocks Number experiments blocks generate DBUDC design. kindExpt = 'DBUDC' data null, blocks mandatory. exptName (optional) Name experiment. locationNames (optional) Names location. multiLocationData (optional) Option pass entry list multiple locations. default multiLocationData = FALSE. data (optional) Data frame 2 columns: ENTRY | NAME .","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/diagonal_arrangement.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Spatial Un-replicated Diagonal Arrangement Design β€” diagonal_arrangement","text":"list five elements. infoDesign list information design parameters. layoutRandom matrix randomization layout. plotsNumber matrix layout plot number. data_entry data frame data input. fieldBook data frame field book design. includes index (Row, Column).","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/diagonal_arrangement.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Spatial Un-replicated Diagonal Arrangement Design β€” diagonal_arrangement","text":"Clarke, G. P. Y., & Stefanova, K. T. (2011). Optimal design early-generation plant breeding trials unreplicated partially replicated test lines. Australian & New Zealand Journal Statistics, 53(4), 461–480.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/diagonal_arrangement.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Spatial Un-replicated Diagonal Arrangement Design β€” diagonal_arrangement","text":"Didier Murillo [aut], Salvador Gezan [aut], Ana Heilman [ctb], Thomas Walk [ctb], Johan Aparicio [ctb], Richard Horsley [ctb]","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/diagonal_arrangement.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Spatial Un-replicated Diagonal Arrangement Design β€” diagonal_arrangement","text":"","code":"# Example 1: Generates a spatial single diagonal arrangement design in one location # with 270 treatments and 30 check plots for a field with dimensions 15 rows x 20 cols # in a serpentine arrangement. spatd <- diagonal_arrangement( nrows = 15, ncols = 20, lines = 270, checks = 4, plotNumber = 101, kindExpt = \"SUDC\", planter = \"serpentine\", seed = 1987, exptName = \"20WRY1\", locationNames = \"MINOT\" ) spatd$infoDesign #> $rows #> [1] 15 #> #> $columns #> [1] 20 #> #> $treatments #> [1] 270 #> #> $checks #> [1] 4 #> #> $entry_checks #> $entry_checks[[1]] #> [1] 1 2 3 4 #> #> #> $rep_checks #> $rep_checks[[1]] #> [1] 8 7 8 7 #> #> #> $locations #> [1] 1 #> #> $planter #> [1] \"serpentine\" #> #> $percent_checks #> [1] \"10%\" #> #> $fillers #> [1] 0 #> #> $seed #> [1] 1987 #> #> $id_design #> [1] 15 #> spatd$layoutRandom #> [[1]] #> Col1 Col2 Col3 Col4 Col5 Col6 Col7 Col8 Col9 Col10 Col11 Col12 Col13 #> Row15 164 3 153 11 221 179 151 139 58 22 266 2 129 #> Row14 89 182 185 38 1 253 156 241 160 252 214 86 130 #> Row13 15 148 82 213 44 194 269 2 265 169 48 245 210 #> Row12 1 124 52 177 5 261 47 40 17 87 3 104 147 #> Row11 100 127 136 4 19 65 158 46 18 229 157 274 59 #> Row10 94 50 27 31 220 166 3 172 170 12 16 176 137 #> Row9 205 212 115 142 110 208 224 216 222 2 246 42 251 #> Row8 175 92 1 197 243 234 236 99 211 67 140 39 3 #> Row7 75 76 8 122 200 1 264 25 138 199 107 120 131 #> Row6 132 93 254 7 247 60 45 171 3 117 103 116 190 #> Row5 181 2 70 79 85 133 203 134 184 273 34 1 174 #> Row4 71 204 159 29 2 83 26 64 119 145 240 223 225 #> Row3 144 231 80 255 43 187 112 4 168 98 32 41 96 #> Row2 4 196 238 235 97 183 111 143 186 237 2 232 263 #> Row1 55 108 248 4 250 217 123 249 126 28 23 118 20 #> Col14 Col15 Col16 Col17 Col18 Col19 Col20 #> Row15 33 109 154 88 30 53 95 #> Row14 163 4 219 68 270 173 90 #> Row13 244 125 149 226 1 54 56 #> Row12 259 233 267 201 193 6 10 #> Row11 2 114 21 77 272 72 24 #> Row10 102 155 36 3 9 162 191 #> Row9 218 106 228 258 167 84 1 #> Row8 230 192 62 135 198 14 69 #> Row7 161 81 3 165 189 268 57 #> Row6 128 146 206 141 215 4 195 #> Row5 61 202 51 242 73 63 207 #> Row4 113 1 78 178 152 37 180 #> Row3 101 74 66 239 4 105 256 #> Row2 49 262 91 257 121 260 209 #> Row1 3 13 150 188 35 227 271 #> spatd$plotsNumber #> [[1]] #> Col1 Col2 Col3 Col4 Col5 Col6 Col7 Col8 Col9 Col10 Col11 Col12 Col13 #> Row15 381 382 383 384 385 386 387 388 389 390 391 392 393 #> Row14 380 379 378 377 376 375 374 373 372 371 370 369 368 #> Row13 341 342 343 344 345 346 347 348 349 350 351 352 353 #> Row12 340 339 338 337 336 335 334 333 332 331 330 329 328 #> Row11 301 302 303 304 305 306 307 308 309 310 311 312 313 #> Row10 300 299 298 297 296 295 294 293 292 291 290 289 288 #> Row9 261 262 263 264 265 266 267 268 269 270 271 272 273 #> Row8 260 259 258 257 256 255 254 253 252 251 250 249 248 #> Row7 221 222 223 224 225 226 227 228 229 230 231 232 233 #> Row6 220 219 218 217 216 215 214 213 212 211 210 209 208 #> Row5 181 182 183 184 185 186 187 188 189 190 191 192 193 #> Row4 180 179 178 177 176 175 174 173 172 171 170 169 168 #> Row3 141 142 143 144 145 146 147 148 149 150 151 152 153 #> Row2 140 139 138 137 136 135 134 133 132 131 130 129 128 #> Row1 101 102 103 104 105 106 107 108 109 110 111 112 113 #> Col14 Col15 Col16 Col17 Col18 Col19 Col20 #> Row15 394 395 396 397 398 399 400 #> Row14 367 366 365 364 363 362 361 #> Row13 354 355 356 357 358 359 360 #> Row12 327 326 325 324 323 322 321 #> Row11 314 315 316 317 318 319 320 #> Row10 287 286 285 284 283 282 281 #> Row9 274 275 276 277 278 279 280 #> Row8 247 246 245 244 243 242 241 #> Row7 234 235 236 237 238 239 240 #> Row6 207 206 205 204 203 202 201 #> Row5 194 195 196 197 198 199 200 #> Row4 167 166 165 164 163 162 161 #> Row3 154 155 156 157 158 159 160 #> Row2 127 126 125 124 123 122 121 #> Row1 114 115 116 117 118 119 120 #> head(spatd$fieldBook, 12) #> ID EXPT LOCATION YEAR PLOT ROW COLUMN CHECKS ENTRY TREATMENT #> 1 1 20WRY1 MINOT 2024 101 1 1 0 55 Gen-55 #> 2 2 20WRY1 MINOT 2024 102 1 2 0 108 Gen-108 #> 3 3 20WRY1 MINOT 2024 103 1 3 0 248 Gen-248 #> 4 4 20WRY1 MINOT 2024 104 1 4 4 4 Check-4 #> 5 5 20WRY1 MINOT 2024 105 1 5 0 250 Gen-250 #> 6 6 20WRY1 MINOT 2024 106 1 6 0 217 Gen-217 #> 7 7 20WRY1 MINOT 2024 107 1 7 0 123 Gen-123 #> 8 8 20WRY1 MINOT 2024 108 1 8 0 249 Gen-249 #> 9 9 20WRY1 MINOT 2024 109 1 9 0 126 Gen-126 #> 10 10 20WRY1 MINOT 2024 110 1 10 0 28 Gen-28 #> 11 11 20WRY1 MINOT 2024 111 1 11 0 23 Gen-23 #> 12 12 20WRY1 MINOT 2024 112 1 12 0 118 Gen-118 # Example 2: Generates a spatial decision block diagonal arrangement design in one location # with 720 treatments allocated in 5 experiments or blocks for a field with dimensions # 30 rows x 26 cols in a serpentine arrangement. In this case, we show how to set up the data # option with the entries list. checks <- 5;expts <- 5 list_checks <- paste(\"CH\", 1:checks, sep = \"\") treatments <- paste(\"G\", 6:725, sep = \"\") treatment_list <- data.frame(list(ENTRY = 1:725, NAME = c(list_checks, treatments))) head(treatment_list, 12) #> ENTRY NAME #> 1 1 CH1 #> 2 2 CH2 #> 3 3 CH3 #> 4 4 CH4 #> 5 5 CH5 #> 6 6 G6 #> 7 7 G7 #> 8 8 G8 #> 9 9 G9 #> 10 10 G10 #> 11 11 G11 #> 12 12 G12 tail(treatment_list, 12) #> ENTRY NAME #> 714 714 G714 #> 715 715 G715 #> 716 716 G716 #> 717 717 G717 #> 718 718 G718 #> 719 719 G719 #> 720 720 G720 #> 721 721 G721 #> 722 722 G722 #> 723 723 G723 #> 724 724 G724 #> 725 725 G725 spatDB <- diagonal_arrangement( nrows = 30, ncols = 26, checks = 5, plotNumber = 1, kindExpt = \"DBUDC\", planter = \"serpentine\", splitBy = \"row\", blocks = c(150,155,95,200,120), data = treatment_list ) spatDB$infoDesign #> $rows #> [1] 30 #> #> $columns #> [1] 26 #> #> $treatments #> [1] 150 155 95 200 120 #> #> $checks #> [1] 5 #> #> $entry_checks #> $entry_checks[[1]] #> [1] 1 2 3 4 5 #> #> #> $rep_checks #> $rep_checks[[1]] #> [1] 10 13 13 11 13 #> #> #> $locations #> [1] 1 #> #> $planter #> [1] \"serpentine\" #> #> $percent_checks #> [1] \"7.7%\" #> #> $fillers #> [1] 0 #> #> $seed #> [1] 24210 #> #> $id_design #> [1] 15 #> spatDB$layoutRandom #> [[1]] #> Col1 Col2 Col3 Col4 Col5 Col6 Col7 Col8 Col9 Col10 Col11 Col12 Col13 #> Row30 702 3 686 699 642 709 701 689 664 720 696 633 708 #> Row29 722 649 616 627 716 4 673 639 711 641 680 688 710 #> Row28 698 615 631 674 672 636 626 685 608 4 651 697 650 #> Row27 5 679 629 677 606 621 692 662 694 725 663 669 666 #> Row26 661 622 610 678 3 612 687 657 713 609 675 670 704 #> Row25 566 576 522 514 491 575 433 598 4 432 473 567 454 #> Row24 529 500 488 518 580 458 526 525 419 480 548 605 5 #> Row23 508 410 602 3 471 588 470 498 492 474 437 472 558 #> Row22 442 541 468 552 463 482 449 2 584 443 423 599 535 #> Row21 446 475 589 467 537 422 542 416 572 435 411 3 487 #> Row20 560 547 3 460 597 429 448 469 590 409 464 506 478 #> Row19 600 530 550 504 520 521 1 461 536 556 486 509 519 #> Row18 436 544 447 424 415 545 543 438 512 595 4 578 534 #> Row17 334 5 340 361 396 345 365 342 384 373 390 392 316 #> Row16 367 366 335 387 404 2 311 395 389 348 328 394 380 #> Row15 397 320 356 351 314 327 339 403 383 5 377 319 374 #> Row14 1 321 331 337 353 402 352 364 358 322 369 329 405 #> Row13 209 256 242 296 5 201 272 237 310 279 158 243 274 #> Row12 186 292 222 193 275 179 200 261 3 252 204 250 289 #> Row11 221 168 176 301 297 184 224 271 244 263 161 188 2 #> Row10 306 206 307 2 300 298 255 278 284 295 259 173 241 #> Row9 302 190 251 170 187 178 293 2 157 230 260 240 159 #> Row8 246 181 189 277 192 232 162 228 305 167 245 5 194 #> Row7 58 41 1 124 57 55 11 199 171 254 291 182 304 #> Row6 96 133 44 81 98 139 2 66 62 7 53 70 20 #> Row5 140 85 65 31 39 106 73 33 76 112 4 34 54 #> Row4 145 3 50 128 64 137 95 42 144 120 92 118 115 #> Row3 107 67 61 149 80 5 101 47 27 77 151 127 74 #> Row2 138 116 122 88 154 117 29 110 78 4 60 83 113 #> Row1 2 94 102 114 26 79 91 131 25 109 8 6 49 #> Col14 Col15 Col16 Col17 Col18 Col19 Col20 Col21 Col22 Col23 Col24 Col25 #> Row30 723 5 655 611 667 700 619 617 721 623 624 635 #> Row29 658 714 706 643 684 1 647 638 648 705 625 719 #> Row28 681 640 652 654 630 715 646 724 637 2 620 718 #> Row27 1 690 607 682 668 613 659 644 628 653 693 634 #> Row26 671 691 614 676 2 632 717 660 665 703 707 618 #> Row25 462 455 408 596 406 479 451 591 3 494 503 513 #> Row24 583 426 453 483 561 496 456 571 430 440 570 459 #> Row23 527 553 466 5 418 524 445 420 450 477 563 452 #> Row22 431 555 585 538 413 417 577 4 485 546 489 551 #> Row21 516 407 594 439 523 604 414 481 532 539 510 1 #> Row20 562 586 2 581 573 515 531 425 501 444 587 421 #> Row19 517 499 507 465 484 495 5 528 559 434 574 579 #> Row18 412 603 476 490 565 511 457 540 582 593 2 568 #> Row17 349 3 336 368 341 569 564 557 493 428 554 502 #> Row16 385 355 323 378 375 4 318 381 399 333 362 401 #> Row15 376 354 391 398 350 338 332 346 330 2 382 313 #> Row14 3 370 386 315 325 371 324 372 379 326 317 312 #> Row13 203 247 285 281 5 215 400 347 363 393 357 388 #> Row12 264 191 286 174 225 269 197 238 1 202 217 164 #> Row11 282 268 223 235 165 180 163 231 183 308 198 299 #> Row10 216 273 177 4 294 156 276 207 169 160 195 229 #> Row9 210 196 233 267 249 227 290 4 205 266 211 258 #> Row8 219 208 280 175 172 309 236 234 239 283 212 1 #> Row7 270 287 5 166 185 213 218 226 220 288 248 265 #> Row6 105 32 84 12 103 16 4 35 19 69 71 87 #> Row5 153 130 30 63 152 150 46 141 68 38 3 10 #> Row4 155 2 125 147 56 132 9 119 59 13 146 121 #> Row3 45 52 21 72 129 1 14 18 43 90 36 75 #> Row2 86 134 24 22 15 143 93 17 97 3 99 100 #> Row1 5 136 37 48 40 111 135 104 123 28 82 148 #> Col26 #> Row30 645 #> Row29 695 #> Row28 712 #> Row27 683 #> Row26 656 #> Row25 497 #> Row24 1 #> Row23 533 #> Row22 427 #> Row21 505 #> Row20 549 #> Row19 592 #> Row18 601 #> Row17 441 #> Row16 343 #> Row15 359 #> Row14 360 #> Row13 344 #> Row12 303 #> Row11 3 #> Row10 214 #> Row9 253 #> Row8 257 #> Row7 262 #> Row6 108 #> Row5 89 #> Row4 51 #> Row3 142 #> Row2 126 #> Row1 23 #> spatDB$plotsNumber #> [[1]] #> Col1 Col2 Col3 Col4 Col5 Col6 Col7 Col8 Col9 Col10 Col11 Col12 Col13 #> Row30 780 779 778 777 776 775 774 773 772 771 770 769 768 #> Row29 729 730 731 732 733 734 735 736 737 738 739 740 741 #> Row28 728 727 726 725 724 723 722 721 720 719 718 717 716 #> Row27 677 678 679 680 681 682 683 684 685 686 687 688 689 #> Row26 676 675 674 673 672 671 670 669 668 667 666 665 664 #> Row25 625 626 627 628 629 630 631 632 633 634 635 636 637 #> Row24 624 623 622 621 620 619 618 617 616 615 614 613 612 #> Row23 573 574 575 576 577 578 579 580 581 582 583 584 585 #> Row22 572 571 570 569 568 567 566 565 564 563 562 561 560 #> Row21 521 522 523 524 525 526 527 528 529 530 531 532 533 #> Row20 520 519 518 517 516 515 514 513 512 511 510 509 508 #> Row19 469 470 471 472 473 474 475 476 477 478 479 480 481 #> Row18 468 467 466 465 464 463 462 461 460 459 458 457 456 #> Row17 417 418 419 420 421 422 423 424 425 426 427 428 429 #> Row16 416 415 414 413 412 411 410 409 408 407 406 405 404 #> Row15 365 366 367 368 369 370 371 372 373 374 375 376 377 #> Row14 364 363 362 361 360 359 358 357 356 355 354 353 352 #> Row13 313 314 315 316 317 318 319 320 321 322 323 324 325 #> Row12 312 311 310 309 308 307 306 305 304 303 302 301 300 #> Row11 261 262 263 264 265 266 267 268 269 270 271 272 273 #> Row10 260 259 258 257 256 255 254 253 252 251 250 249 248 #> Row9 209 210 211 212 213 214 215 216 217 218 219 220 221 #> Row8 208 207 206 205 204 203 202 201 200 199 198 197 196 #> Row7 157 158 159 160 161 162 163 164 165 166 167 168 169 #> Row6 156 155 154 153 152 151 150 149 148 147 146 145 144 #> Row5 105 106 107 108 109 110 111 112 113 114 115 116 117 #> Row4 104 103 102 101 100 99 98 97 96 95 94 93 92 #> Row3 53 54 55 56 57 58 59 60 61 62 63 64 65 #> Row2 52 51 50 49 48 47 46 45 44 43 42 41 40 #> Row1 1 2 3 4 5 6 7 8 9 10 11 12 13 #> Col14 Col15 Col16 Col17 Col18 Col19 Col20 Col21 Col22 Col23 Col24 Col25 #> Row30 767 766 765 764 763 762 761 760 759 758 757 756 #> Row29 742 743 744 745 746 747 748 749 750 751 752 753 #> Row28 715 714 713 712 711 710 709 708 707 706 705 704 #> Row27 690 691 692 693 694 695 696 697 698 699 700 701 #> Row26 663 662 661 660 659 658 657 656 655 654 653 652 #> Row25 638 639 640 641 642 643 644 645 646 647 648 649 #> Row24 611 610 609 608 607 606 605 604 603 602 601 600 #> Row23 586 587 588 589 590 591 592 593 594 595 596 597 #> Row22 559 558 557 556 555 554 553 552 551 550 549 548 #> Row21 534 535 536 537 538 539 540 541 542 543 544 545 #> Row20 507 506 505 504 503 502 501 500 499 498 497 496 #> Row19 482 483 484 485 486 487 488 489 490 491 492 493 #> Row18 455 454 453 452 451 450 449 448 447 446 445 444 #> Row17 430 431 432 433 434 435 436 437 438 439 440 441 #> Row16 403 402 401 400 399 398 397 396 395 394 393 392 #> Row15 378 379 380 381 382 383 384 385 386 387 388 389 #> Row14 351 350 349 348 347 346 345 344 343 342 341 340 #> Row13 326 327 328 329 330 331 332 333 334 335 336 337 #> Row12 299 298 297 296 295 294 293 292 291 290 289 288 #> Row11 274 275 276 277 278 279 280 281 282 283 284 285 #> Row10 247 246 245 244 243 242 241 240 239 238 237 236 #> Row9 222 223 224 225 226 227 228 229 230 231 232 233 #> Row8 195 194 193 192 191 190 189 188 187 186 185 184 #> Row7 170 171 172 173 174 175 176 177 178 179 180 181 #> Row6 143 142 141 140 139 138 137 136 135 134 133 132 #> Row5 118 119 120 121 122 123 124 125 126 127 128 129 #> Row4 91 90 89 88 87 86 85 84 83 82 81 80 #> Row3 66 67 68 69 70 71 72 73 74 75 76 77 #> Row2 39 38 37 36 35 34 33 32 31 30 29 28 #> Row1 14 15 16 17 18 19 20 21 22 23 24 25 #> Col26 #> Row30 755 #> Row29 754 #> Row28 703 #> Row27 702 #> Row26 651 #> Row25 650 #> Row24 599 #> Row23 598 #> Row22 547 #> Row21 546 #> Row20 495 #> Row19 494 #> Row18 443 #> Row17 442 #> Row16 391 #> Row15 390 #> Row14 339 #> Row13 338 #> Row12 287 #> Row11 286 #> Row10 235 #> Row9 234 #> Row8 183 #> Row7 182 #> Row6 131 #> Row5 130 #> Row4 79 #> Row3 78 #> Row2 27 #> Row1 26 #> head(spatDB$fieldBook,12) #> ID EXPT LOCATION YEAR PLOT ROW COLUMN CHECKS ENTRY TREATMENT #> 1 1 Block1 1 2024 1 1 1 2 2 CH2 #> 2 2 Block1 1 2024 2 1 2 0 94 G94 #> 3 3 Block1 1 2024 3 1 3 0 102 G102 #> 4 4 Block1 1 2024 4 1 4 0 114 G114 #> 5 5 Block1 1 2024 5 1 5 0 26 G26 #> 6 6 Block1 1 2024 6 1 6 0 79 G79 #> 7 7 Block1 1 2024 7 1 7 0 91 G91 #> 8 8 Block1 1 2024 8 1 8 0 131 G131 #> 9 9 Block1 1 2024 9 1 9 0 25 G25 #> 10 10 Block1 1 2024 10 1 10 0 109 G109 #> 11 11 Block1 1 2024 11 1 11 0 8 G8 #> 12 12 Block1 1 2024 12 1 12 0 6 G6 # Example 3: Generates a spatial decision block diagonal arrangement design in one location # with 270 treatments allocated in 3 experiments or blocks for a field with dimensions # 20 rows x 15 cols in a serpentine arrangement. Which in turn is an augmented block (3 blocks). spatAB <- diagonal_arrangement( nrows = 20, ncols = 15, lines = 270, checks = 4, plotNumber = c(1,1001,2001), kindExpt = \"DBUDC\", planter = \"serpentine\", exptName = c(\"20WRA\", \"20WRB\", \"20WRC\"), blocks = c(90, 90, 90), splitBy = \"column\" ) spatAB$infoDesign #> $rows #> [1] 20 #> #> $columns #> [1] 15 #> #> $treatments #> [1] 90 90 90 #> #> $checks #> [1] 4 #> #> $entry_checks #> $entry_checks[[1]] #> [1] 1 2 3 4 #> #> #> $rep_checks #> $rep_checks[[1]] #> [1] 7 6 8 9 #> #> #> $locations #> [1] 1 #> #> $planter #> [1] \"serpentine\" #> #> $percent_checks #> [1] \"10%\" #> #> $fillers #> [1] 0 #> #> $seed #> [1] 72391 #> #> $id_design #> [1] 15 #> spatAB$layoutRandom #> [[1]] #> Col1 Col2 Col3 Col4 Col5 Col6 Col7 Col8 Col9 Col10 Col11 Col12 Col13 #> Row20 82 1 56 57 70 144 172 148 146 173 219 4 229 #> Row19 49 42 69 21 3 103 111 151 102 157 237 249 221 #> Row18 44 10 9 29 12 109 177 3 106 149 262 225 226 #> Row17 2 43 90 16 71 162 176 136 104 138 4 191 185 #> Row16 75 88 36 1 30 110 133 175 180 158 193 253 186 #> Row15 85 45 91 19 13 97 2 171 154 105 252 204 245 #> Row14 86 67 14 83 93 134 161 100 140 4 211 217 251 #> Row13 33 92 3 6 5 116 160 101 117 98 227 263 2 #> Row12 54 31 74 89 8 3 181 145 99 114 254 223 267 #> Row11 23 7 50 25 76 137 163 168 2 159 242 260 206 #> Row10 63 4 11 24 61 139 182 167 153 130 208 3 216 #> Row9 80 65 58 52 1 118 135 125 122 147 192 264 234 #> Row8 32 26 41 48 39 152 183 4 165 95 233 220 240 #> Row7 4 66 37 68 46 178 142 132 96 115 1 198 258 #> Row6 55 27 35 2 77 155 166 131 127 169 209 212 256 #> Row5 94 18 60 34 40 141 3 119 126 184 231 224 241 #> Row4 59 15 73 38 84 179 113 170 164 1 232 189 235 #> Row3 28 47 4 53 51 143 123 120 129 108 244 207 4 #> Row2 64 20 17 72 81 4 107 150 174 156 210 230 222 #> Row1 79 62 78 22 87 121 124 112 1 128 228 261 213 #> Col14 Col15 #> Row20 243 259 #> Row19 248 3 #> Row18 246 196 #> Row17 269 265 #> Row16 2 190 #> Row15 214 257 #> Row14 188 266 #> Row13 272 255 #> Row12 201 203 #> Row11 202 270 #> Row10 205 215 #> Row9 268 3 #> Row8 218 197 #> Row7 247 250 #> Row6 1 187 #> Row5 273 239 #> Row4 236 274 #> Row3 271 195 #> Row2 200 199 #> Row1 238 194 #> spatAB$plotsNumber #> [[1]] #> Col1 Col2 Col3 Col4 Col5 Col6 Col7 Col8 Col9 Col10 Col11 Col12 Col13 #> Row20 100 99 98 97 96 1100 1099 1098 1097 1096 2100 2099 2098 #> Row19 91 92 93 94 95 1091 1092 1093 1094 1095 2091 2092 2093 #> Row18 90 89 88 87 86 1090 1089 1088 1087 1086 2090 2089 2088 #> Row17 81 82 83 84 85 1081 1082 1083 1084 1085 2081 2082 2083 #> Row16 80 79 78 77 76 1080 1079 1078 1077 1076 2080 2079 2078 #> Row15 71 72 73 74 75 1071 1072 1073 1074 1075 2071 2072 2073 #> Row14 70 69 68 67 66 1070 1069 1068 1067 1066 2070 2069 2068 #> Row13 61 62 63 64 65 1061 1062 1063 1064 1065 2061 2062 2063 #> Row12 60 59 58 57 56 1060 1059 1058 1057 1056 2060 2059 2058 #> Row11 51 52 53 54 55 1051 1052 1053 1054 1055 2051 2052 2053 #> Row10 50 49 48 47 46 1050 1049 1048 1047 1046 2050 2049 2048 #> Row9 41 42 43 44 45 1041 1042 1043 1044 1045 2041 2042 2043 #> Row8 40 39 38 37 36 1040 1039 1038 1037 1036 2040 2039 2038 #> Row7 31 32 33 34 35 1031 1032 1033 1034 1035 2031 2032 2033 #> Row6 30 29 28 27 26 1030 1029 1028 1027 1026 2030 2029 2028 #> Row5 21 22 23 24 25 1021 1022 1023 1024 1025 2021 2022 2023 #> Row4 20 19 18 17 16 1020 1019 1018 1017 1016 2020 2019 2018 #> Row3 11 12 13 14 15 1011 1012 1013 1014 1015 2011 2012 2013 #> Row2 10 9 8 7 6 1010 1009 1008 1007 1006 2010 2009 2008 #> Row1 1 2 3 4 5 1001 1002 1003 1004 1005 2001 2002 2003 #> Col14 Col15 #> Row20 2097 2096 #> Row19 2094 2095 #> Row18 2087 2086 #> Row17 2084 2085 #> Row16 2077 2076 #> Row15 2074 2075 #> Row14 2067 2066 #> Row13 2064 2065 #> Row12 2057 2056 #> Row11 2054 2055 #> Row10 2047 2046 #> Row9 2044 2045 #> Row8 2037 2036 #> Row7 2034 2035 #> Row6 2027 2026 #> Row5 2024 2025 #> Row4 2017 2016 #> Row3 2014 2015 #> Row2 2007 2006 #> Row1 2004 2005 #> head(spatAB$fieldBook,12) #> ID EXPT LOCATION YEAR PLOT ROW COLUMN CHECKS ENTRY TREATMENT #> 1 1 20WRA 1 2024 1 1 1 0 79 Gen-79 #> 2 2 20WRA 1 2024 2 1 2 0 62 Gen-62 #> 3 3 20WRA 1 2024 3 1 3 0 78 Gen-78 #> 4 4 20WRA 1 2024 4 1 4 0 22 Gen-22 #> 5 5 20WRA 1 2024 5 1 5 0 87 Gen-87 #> 6 6 20WRB 1 2024 1001 1 6 0 121 Gen-121 #> 7 7 20WRB 1 2024 1002 1 7 0 124 Gen-124 #> 8 8 20WRB 1 2024 1003 1 8 0 112 Gen-112 #> 9 9 20WRB 1 2024 1004 1 9 1 1 Check-1 #> 10 10 20WRB 1 2024 1005 1 10 0 128 Gen-128 #> 11 11 20WRC 1 2024 2001 1 11 0 228 Gen-228 #> 12 12 20WRC 1 2024 2002 1 12 0 261 Gen-261"},{"path":"https://didiermurillof.github.io/FielDHub/reference/do_optim.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate the sparse or p-rep allocation to multiple locations. β€” do_optim","title":"Generate the sparse or p-rep allocation to multiple locations. β€” do_optim","text":"Generate sparse p-rep allocation multiple locations.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/do_optim.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate the sparse or p-rep allocation to multiple locations. β€” do_optim","text":"","code":"do_optim( design = \"sparse\", lines, l, copies_per_entry, add_checks = FALSE, checks = NULL, rep_checks = NULL, force_balance = TRUE, seed, data = NULL )"},{"path":"https://didiermurillof.github.io/FielDHub/reference/do_optim.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate the sparse or p-rep allocation to multiple locations. β€” do_optim","text":"design Type experimental design. can prep sparse lines Number genotypes, experimental lines treatments. l Number locations sites. default l = 1. copies_per_entry Number copies per plant. design sparse copies_per_entry less l add_checks Option add checks. Optional design = \"prep\" checks Number genotypes checks. rep_checks Replication check. force_balance Get balanced unbalanced locations. default force_balance = TRUE. seed (optional) Real number specifies starting seed obtain reproducible designs. data (optional) Data frame 2 columns: ENTRY | NAME . ENTRY must numeric.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/do_optim.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate the sparse or p-rep allocation to multiple locations. β€” do_optim","text":"list three elements. list_locs list location list entries. allocation matrix allocation treatments. size_locations data frame one column location one row size location.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/do_optim.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Generate the sparse or p-rep allocation to multiple locations. β€” do_optim","text":"Edmondson, R.N. Multi-level Block Designs Comparative Experiments. JABES 25, 500–522 (2020). https://doi.org/10.1007/s13253-020-00416-0","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/do_optim.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Generate the sparse or p-rep allocation to multiple locations. β€” do_optim","text":"Didier Murillo [aut], Salvador Gezan [aut], Ana Heilman [ctb]","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/do_optim.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate the sparse or p-rep allocation to multiple locations. β€” do_optim","text":"","code":"sparse_example <- do_optim( design = \"sparse\", lines = 120, l = 4, copies_per_entry = 3, add_checks = TRUE, checks = 4, seed = 15 )"},{"path":"https://didiermurillof.github.io/FielDHub/reference/full_factorial.html","id":null,"dir":"Reference","previous_headings":"","what":"Generates a Full Factorial Design β€” full_factorial","title":"Generates a Full Factorial Design β€” full_factorial","text":"randomly generates full factorial design across locations.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/full_factorial.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generates a Full Factorial Design β€” full_factorial","text":"","code":"full_factorial( setfactors = NULL, reps = NULL, l = 1, type = 2, plotNumber = 101, continuous = FALSE, planter = \"serpentine\", seed = NULL, locationNames = NULL, factorLabels = TRUE, data = NULL )"},{"path":"https://didiermurillof.github.io/FielDHub/reference/full_factorial.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generates a Full Factorial Design β€” full_factorial","text":"setfactors Numeric vector levels factor. reps Number replicates (full blocks). l Number locations. default l = 1. type Option CRD RCBD designs. Values type = 1 (CRD) type = 2 (RCBD). default type = 2. plotNumber Numeric vector starting plot number location. default plotNumber = 101. continuous Logical plot number continuous . default continuous = FALSE. planter Option serpentine cartesian plot arrangement. default planter = 'serpentine'. seed (optional) Real number specifies starting seed obtain reproducible designs. locationNames (optional) Names location. factorLabels (optional) TRUE retain levels labels original data set otherwise, numeric labels assigned. Default factorLabels =TRUE. data (optional) Data frame labels factors.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/full_factorial.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generates a Full Factorial Design β€” full_factorial","text":"list two elements. infoDesign list information design parameters. fieldBook data frame full factorial field book.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/full_factorial.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Generates a Full Factorial Design β€” full_factorial","text":"Federer, W. T. (1955). Experimental Design. Theory Application. New York, USA. Macmillan Company.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/full_factorial.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Generates a Full Factorial Design β€” full_factorial","text":"Didier Murillo [aut], Salvador Gezan [aut], Ana Heilman [ctb], Thomas Walk [ctb], Johan Aparicio [ctb], Richard Horsley [ctb]","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/full_factorial.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generates a Full Factorial Design β€” full_factorial","text":"","code":"# Example 1: Generates a full factorial with 3 factors each with 2 levels. # This in an RCBD arrangement with 3 reps. fullFact1 <- full_factorial(setfactors = c(2,2,2), reps = 3, l = 1, type = 2, plotNumber = 101, continuous = TRUE, planter = \"serpentine\", seed = 325, locationNames = \"FARGO\") fullFact1$infoDesign #> $factors #> [1] \"A\" \"B\" \"C\" #> #> $levels #> [1] 0 1 0 1 0 1 #> #> $runs #> [1] 8 #> #> $all_treatments #> A B C #> 1 0 0 0 #> 2 1 0 0 #> 3 0 1 0 #> 4 1 1 0 #> 5 0 0 1 #> 6 1 0 1 #> 7 0 1 1 #> 8 1 1 1 #> #> $reps #> [1] 3 #> #> $locations #> [1] 1 #> #> $location_names #> [1] \"FARGO\" #> #> $kind #> [1] \"RCBD\" #> #> $levels_each_factor #> [1] 2 2 2 #> #> $id_design #> [1] 4 #> head(fullFact1$fieldBook,10) #> ID LOCATION PLOT REP FACTOR_A FACTOR_B FACTOR_C TRT_COMB #> 1 1 FARGO 101 1 0 1 1 0*1*1 #> 2 2 FARGO 102 1 1 1 1 1*1*1 #> 3 3 FARGO 103 1 1 0 0 1*0*0 #> 4 4 FARGO 104 1 0 1 0 0*1*0 #> 5 5 FARGO 105 1 1 1 0 1*1*0 #> 6 6 FARGO 106 1 1 0 1 1*0*1 #> 7 7 FARGO 107 1 0 0 0 0*0*0 #> 8 8 FARGO 108 1 0 0 1 0*0*1 #> 16 9 FARGO 109 2 1 1 0 1*1*0 #> 15 10 FARGO 110 2 0 0 0 0*0*0 # Example 2: Generates a full factorial with 3 factors and each with levels: 2,3, # and 2, respectively. In this case, we show how to use the option data FACTORS <- rep(c(\"A\", \"B\", \"C\"), c(2,3,2)) LEVELS <- c(\"a0\", \"a1\", \"b0\", \"b1\", \"b2\", \"c0\", \"c1\") data_factorial <- data.frame(list(FACTOR = FACTORS, LEVEL = LEVELS)) print(data_factorial) #> FACTOR LEVEL #> 1 A a0 #> 2 A a1 #> 3 B b0 #> 4 B b1 #> 5 B b2 #> 6 C c0 #> 7 C c1 # This in an RCBD arrangement with 5 reps in 3 locations. fullFact2 <- full_factorial(setfactors = NULL, reps = 5, l = 3, type = 2, plotNumber = c(101,1001,2001), continuous = FALSE, planter = \"serpentine\", seed = 326, locationNames = c(\"Loc1\",\"Loc2\",\"Loc3\"), data = data_factorial) fullFact2$infoDesign #> $factors #> [1] \"A\" \"B\" \"C\" #> #> $levels #> $levels[[1]] #> [1] \"a0\" \"a1\" #> #> $levels[[2]] #> [1] \"b0\" \"b1\" \"b2\" #> #> $levels[[3]] #> [1] \"c0\" \"c1\" #> #> #> $runs #> [1] 12 #> #> $all_treatments #> A B C #> 1 a0 b0 c0 #> 2 a1 b0 c0 #> 3 a0 b1 c0 #> 4 a1 b1 c0 #> 5 a0 b2 c0 #> 6 a1 b2 c0 #> 7 a0 b0 c1 #> 8 a1 b0 c1 #> 9 a0 b1 c1 #> 10 a1 b1 c1 #> 11 a0 b2 c1 #> 12 a1 b2 c1 #> #> $reps #> [1] 5 #> #> $locations #> [1] 3 #> #> $location_names #> [1] \"Loc1\" \"Loc2\" \"Loc3\" #> #> $kind #> [1] \"RCBD\" #> #> $levels_each_factor #> [1] 2 3 2 #> #> $id_design #> [1] 4 #> head(fullFact2$fieldBook,10) #> ID LOCATION PLOT REP FACTOR_A FACTOR_B FACTOR_C TRT_COMB #> 1 1 Loc1 101 1 a0 b1 c0 a0*b1*c0 #> 2 2 Loc1 102 1 a1 b0 c1 a1*b0*c1 #> 3 3 Loc1 103 1 a1 b2 c1 a1*b2*c1 #> 4 4 Loc1 104 1 a0 b1 c1 a0*b1*c1 #> 5 5 Loc1 105 1 a1 b0 c0 a1*b0*c0 #> 6 6 Loc1 106 1 a0 b0 c1 a0*b0*c1 #> 7 7 Loc1 107 1 a1 b1 c0 a1*b1*c0 #> 8 8 Loc1 108 1 a0 b2 c1 a0*b2*c1 #> 9 9 Loc1 109 1 a1 b1 c1 a1*b1*c1 #> 10 10 Loc1 110 1 a0 b0 c0 a0*b0*c0"},{"path":"https://didiermurillof.github.io/FielDHub/reference/incomplete_blocks.html","id":null,"dir":"Reference","previous_headings":"","what":"Generates a Resolvable Incomplete Block Design β€” incomplete_blocks","title":"Generates a Resolvable Incomplete Block Design β€” incomplete_blocks","text":"Randomly generates resolvable incomplete block design (IBD) characteristics (t, k, r). randomization can done across locations.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/incomplete_blocks.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generates a Resolvable Incomplete Block Design β€” incomplete_blocks","text":"","code":"incomplete_blocks( t = NULL, k = NULL, r = NULL, l = 1, plotNumber = 101, locationNames = NULL, seed = NULL, data = NULL )"},{"path":"https://didiermurillof.github.io/FielDHub/reference/incomplete_blocks.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generates a Resolvable Incomplete Block Design β€” incomplete_blocks","text":"t Number treatments. k Size incomplete blocks (number units per incomplete block). r Number full blocks (resolvable replicates) (also number replicates per treatment). l Number locations. default l = 1. plotNumber Numeric vector starting plot number location. default plotNumber = 101. locationNames (optional) Names location. seed (optional) Real number specifies starting seed obtain reproducible designs. data (optional) Data frame label list treatments.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/incomplete_blocks.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generates a Resolvable Incomplete Block Design β€” incomplete_blocks","text":"list two elements. infoDesign list information design parameters. fieldBook data frame incomplete block design field book.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/incomplete_blocks.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Generates a Resolvable Incomplete Block Design β€” incomplete_blocks","text":"Edmondson., R. N. (2021). blocksdesign: Nested crossed block designs factorial unstructured treatment sets. https://CRAN.R-project.org/package=blocksdesign","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/incomplete_blocks.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Generates a Resolvable Incomplete Block Design β€” incomplete_blocks","text":"Didier Murillo [aut], Salvador Gezan [aut], Ana Heilman [ctb], Thomas Walk [ctb], Johan Aparicio [ctb], Richard Horsley [ctb]","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/incomplete_blocks.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generates a Resolvable Incomplete Block Design β€” incomplete_blocks","text":"","code":"# Example 1: Generates a resolvable IBD of characteristics (t,k,r) = (12,4,2). # 1-resolvable IBDs ibd1 <- incomplete_blocks(t = 12, k = 4, r = 2, seed = 1984) ibd1$infoDesign #> $Reps #> [1] 2 #> #> $iBlocks #> [1] 3 #> #> $NumberTreatments #> [1] 12 #> #> $NumberLocations #> [1] 1 #> #> $Locations #> [1] 1 #> #> $seed #> [1] 1984 #> #> $lambda #> [1] 0.5454545 #> #> $id_design #> [1] 8 #> head(ibd1$fieldBook) #> ID LOCATION PLOT REP IBLOCK UNIT ENTRY TREATMENT #> 1 1 1 101 1 1 1 9 G-9 #> 2 2 1 102 1 1 2 5 G-5 #> 3 3 1 103 1 1 3 6 G-6 #> 4 4 1 104 1 1 4 12 G-12 #> 5 5 1 105 1 2 1 2 G-2 #> 6 6 1 106 1 2 2 11 G-11 # Example 2: Generates a balanced resolvable IBD of characteristics (t,k,r) = (15,3,7). # In this case, we show how to use the option data. treatments <- paste(\"TX-\", 1:15, sep = \"\") ENTRY <- 1:15 treatment_list <- data.frame(list(ENTRY = ENTRY, TREATMENT = treatments)) head(treatment_list) #> ENTRY TREATMENT #> 1 1 TX-1 #> 2 2 TX-2 #> 3 3 TX-3 #> 4 4 TX-4 #> 5 5 TX-5 #> 6 6 TX-6 ibd2 <- incomplete_blocks(t = 15, k = 3, r = 7, seed = 1985, data = treatment_list) ibd2$infoDesign #> $Reps #> [1] 7 #> #> $iBlocks #> [1] 5 #> #> $NumberTreatments #> [1] 15 #> #> $NumberLocations #> [1] 1 #> #> $Locations #> [1] 1 #> #> $seed #> [1] 1985 #> #> $lambda #> [1] 1 #> #> $id_design #> [1] 8 #> head(ibd2$fieldBook) #> ID LOCATION PLOT REP IBLOCK UNIT ENTRY TREATMENT #> 1 1 1 101 1 1 1 1 TX-1 #> 2 2 1 102 1 1 2 11 TX-11 #> 3 3 1 103 1 1 3 13 TX-13 #> 4 4 1 104 1 2 1 3 TX-3 #> 5 5 1 105 1 2 2 14 TX-14 #> 6 6 1 106 1 2 3 4 TX-4"},{"path":"https://didiermurillof.github.io/FielDHub/reference/latin_square.html","id":null,"dir":"Reference","previous_headings":"","what":"Generates a Latin Square Design β€” latin_square","title":"Generates a Latin Square Design β€” latin_square","text":"Randomly generates latin square design 10 treatments.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/latin_square.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generates a Latin Square Design β€” latin_square","text":"","code":"latin_square( t = NULL, reps = 1, plotNumber = 101, planter = \"serpentine\", seed = NULL, locationNames = NULL, data = NULL )"},{"path":"https://didiermurillof.github.io/FielDHub/reference/latin_square.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generates a Latin Square Design β€” latin_square","text":"t Number treatments. reps Number full resolvable squares. default reps = 1. plotNumber Starting plot number. default plotNumber = 101. planter Option serpentine cartesian arrangement. default planter = 'serpentine'. seed (optional) Real number specifies starting seed obtain reproducible designs. locationNames (optional) Name location. data (optional) Data frame label list treatments.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/latin_square.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generates a Latin Square Design β€” latin_square","text":"list information design parameters. Data frame latin square field book. list two elements. infoDesign list information design parameters. fieldBook data frame latin square field book.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/latin_square.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Generates a Latin Square Design β€” latin_square","text":"Federer, W. T. (1955). Experimental Design. Theory Application. New York, USA. Macmillan Company.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/latin_square.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Generates a Latin Square Design β€” latin_square","text":"Didier Murillo [aut], Salvador Gezan [aut], Ana Heilman [ctb], Thomas Walk [ctb], Johan Aparicio [ctb], Thiago de Paula Oliveira[ctb] Richard Horsley [ctb]","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/latin_square.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generates a Latin Square Design β€” latin_square","text":"","code":"# Example 1: Generates a latin square design with 4 treatments and 2 reps. latinSq1 <- latin_square(t = 4, reps = 2, plotNumber = 101, planter = \"cartesian\", seed = 1980) print(latinSq1) #> Latin Square Design: #> #> Information on the design parameters: #> List of 4 #> $ treatments : int 4 #> $ squares : num 2 #> $ locationName: NULL #> $ seed : num 1980 #> #> 10 First observations of the data frame with the latin_square field book: #> ID LOCATION PLOT SQUARE ROW COLUMN TREATMENT #> 1 1 1 101 1 Row 1 Column 1 T1 #> 2 2 1 102 1 Row 1 Column 2 T4 #> 3 3 1 103 1 Row 1 Column 3 T2 #> 4 4 1 104 1 Row 1 Column 4 T3 #> 5 5 1 105 1 Row 2 Column 1 T3 #> 6 6 1 106 1 Row 2 Column 2 T1 #> 7 7 1 107 1 Row 2 Column 3 T4 #> 8 8 1 108 1 Row 2 Column 4 T2 #> 9 9 1 109 1 Row 3 Column 1 T4 #> 10 10 1 110 1 Row 3 Column 2 T2 summary(latinSq1) #> Latin Square Design: #> #> 1. Information on the design parameters: #> List of 5 #> $ treatments : int 4 #> $ squares : num 2 #> $ locationName: NULL #> $ seed : num 1980 #> $ id_design : num 3 #> #> 2. Squares: #> $rep1 #> Column 1 Column 2 Column 3 Column 4 #> Row 1 \"T1\" \"T4\" \"T2\" \"T3\" #> Row 2 \"T3\" \"T1\" \"T4\" \"T2\" #> Row 3 \"T4\" \"T2\" \"T3\" \"T1\" #> Row 4 \"T2\" \"T3\" \"T1\" \"T4\" #> #> $rep2 #> Column 1 Column 2 Column 3 Column 4 #> Row 1 \"T1\" \"T3\" \"T4\" \"T2\" #> Row 2 \"T2\" \"T4\" \"T3\" \"T1\" #> Row 3 \"T4\" \"T1\" \"T2\" \"T3\" #> Row 4 \"T3\" \"T2\" \"T1\" \"T4\" #> #> #> 3. Plot squares: #> $rep1 #> [,1] [,2] [,3] [,4] #> [1,] 101 102 103 104 #> [2,] 105 106 107 108 #> [3,] 109 110 111 112 #> [4,] 113 114 115 116 #> #> $rep2 #> [,1] [,2] [,3] [,4] #> [1,] 201 202 203 204 #> [2,] 205 206 207 208 #> [3,] 209 210 211 212 #> [4,] 213 214 215 216 #> #> #> 4. Structure of the data frame with the latin_square field book: #> #> 'data.frame':\t32 obs. of 7 variables: #> $ ID : int 1 2 3 4 5 6 7 8 9 10 ... #> $ LOCATION : int 1 1 1 1 1 1 1 1 1 1 ... #> $ PLOT : int 101 102 103 104 105 106 107 108 109 110 ... #> $ SQUARE : int 1 1 1 1 1 1 1 1 1 1 ... #> $ ROW : Factor w/ 4 levels \"Row 1\",\"Row 2\",..: 1 1 1 1 2 2 2 2 3 3 ... #> $ COLUMN : Factor w/ 4 levels \"Column 1\",\"Column 2\",..: 1 2 3 4 1 2 3 4 1 2 ... #> $ TREATMENT: chr \"T1\" \"T4\" \"T2\" \"T3\" ... head(latinSq1$fieldBook) #> ID LOCATION PLOT SQUARE ROW COLUMN TREATMENT #> 1 1 1 101 1 Row 1 Column 1 T1 #> 2 2 1 102 1 Row 1 Column 2 T4 #> 3 3 1 103 1 Row 1 Column 3 T2 #> 4 4 1 104 1 Row 1 Column 4 T3 #> 5 5 1 105 1 Row 2 Column 1 T3 #> 6 6 1 106 1 Row 2 Column 2 T1 # Example 2: Generates a latin square design with 5 treatments and 3 reps. latin_data <- data.frame(list(ROW = paste(\"Period\", 1:5, sep = \"\"), COLUMN = paste(\"Cow\", 1:5, sep = \"\"), TREATMENT = paste(\"Diet\", 1:5, sep = \"\"))) print(latin_data) #> ROW COLUMN TREATMENT #> 1 Period1 Cow1 Diet1 #> 2 Period2 Cow2 Diet2 #> 3 Period3 Cow3 Diet3 #> 4 Period4 Cow4 Diet4 #> 5 Period5 Cow5 Diet5 latinSq2 <- latin_square(t = NULL, reps = 3, plotNumber = 101, planter = \"cartesian\", seed = 1981, data = latin_data) latinSq2$squares #> $rep1 #> Cow1 Cow2 Cow3 Cow4 Cow5 #> Period1 \"Diet4\" \"Diet3\" \"Diet2\" \"Diet1\" \"Diet5\" #> Period2 \"Diet1\" \"Diet2\" \"Diet4\" \"Diet5\" \"Diet3\" #> Period3 \"Diet3\" \"Diet5\" \"Diet1\" \"Diet2\" \"Diet4\" #> Period4 \"Diet2\" \"Diet4\" \"Diet5\" \"Diet3\" \"Diet1\" #> Period5 \"Diet5\" \"Diet1\" \"Diet3\" \"Diet4\" \"Diet2\" #> #> $rep2 #> Cow1 Cow2 Cow3 Cow4 Cow5 #> Period1 \"Diet2\" \"Diet4\" \"Diet5\" \"Diet3\" \"Diet1\" #> Period2 \"Diet1\" \"Diet2\" \"Diet3\" \"Diet4\" \"Diet5\" #> Period3 \"Diet3\" \"Diet5\" \"Diet1\" \"Diet2\" \"Diet4\" #> Period4 \"Diet4\" \"Diet1\" \"Diet2\" \"Diet5\" \"Diet3\" #> Period5 \"Diet5\" \"Diet3\" \"Diet4\" \"Diet1\" \"Diet2\" #> #> $rep3 #> Cow1 Cow2 Cow3 Cow4 Cow5 #> Period1 \"Diet4\" \"Diet2\" \"Diet1\" \"Diet3\" \"Diet5\" #> Period2 \"Diet1\" \"Diet3\" \"Diet2\" \"Diet5\" \"Diet4\" #> Period3 \"Diet3\" \"Diet5\" \"Diet4\" \"Diet2\" \"Diet1\" #> Period4 \"Diet5\" \"Diet1\" \"Diet3\" \"Diet4\" \"Diet2\" #> Period5 \"Diet2\" \"Diet4\" \"Diet5\" \"Diet1\" \"Diet3\" #> latinSq2$plotSquares #> $rep1 #> [,1] [,2] [,3] [,4] [,5] #> [1,] 101 102 103 104 105 #> [2,] 106 107 108 109 110 #> [3,] 111 112 113 114 115 #> [4,] 116 117 118 119 120 #> [5,] 121 122 123 124 125 #> #> $rep2 #> [,1] [,2] [,3] [,4] [,5] #> [1,] 201 202 203 204 205 #> [2,] 206 207 208 209 210 #> [3,] 211 212 213 214 215 #> [4,] 216 217 218 219 220 #> [5,] 221 222 223 224 225 #> #> $rep3 #> [,1] [,2] [,3] [,4] [,5] #> [1,] 301 302 303 304 305 #> [2,] 306 307 308 309 310 #> [3,] 311 312 313 314 315 #> [4,] 316 317 318 319 320 #> [5,] 321 322 323 324 325 #> head(latinSq2$fieldBook) #> ID LOCATION PLOT SQUARE ROW COLUMN TREATMENT #> 1 1 1 101 1 Period1 Cow1 Diet4 #> 2 2 1 102 1 Period1 Cow2 Diet3 #> 3 3 1 103 1 Period1 Cow3 Diet2 #> 4 4 1 104 1 Period1 Cow4 Diet1 #> 5 5 1 105 1 Period1 Cow5 Diet5 #> 6 6 1 106 1 Period2 Cow1 Diet1"},{"path":"https://didiermurillof.github.io/FielDHub/reference/multi_location_prep.html","id":null,"dir":"Reference","previous_headings":"","what":"Optimized multi-location partially replicated design β€” multi_location_prep","title":"Optimized multi-location partially replicated design β€” multi_location_prep","text":"Optimized multi-location partially replicated design","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/multi_location_prep.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Optimized multi-location partially replicated design β€” multi_location_prep","text":"","code":"multi_location_prep( lines, nrows, ncols, l, planter = \"serpentine\", plotNumber, desired_avg, copies_per_entry, checks = NULL, rep_checks = NULL, exptName, locationNames, optim_list, seed, data = NULL )"},{"path":"https://didiermurillof.github.io/FielDHub/reference/multi_location_prep.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Optimized multi-location partially replicated design β€” multi_location_prep","text":"lines Number genotypes, experimental lines treatments. nrows Numeric vector number rows field location. ncols Numeric vector number columns field location. l Number locations. default l = 1. planter Option serpentine cartesian movement. default planter = 'serpentine'. plotNumber Numeric vector starting plot number location. default plotNumber = 101. desired_avg (optional) Desired average treatments across locations. copies_per_entry Number total copies per treatment. checks Number checks. rep_checks Number replications per check. exptName (optional) Name experiment. locationNames (optional) Name location. optim_list (optional) list object class \"MultiPrep\"generated do_optim() function. seed (optional) Real number specifies starting seed obtain reproducible designs. data (optional) Data frame 2 columns: ENTRY | NAME . ENTRY must numeric.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/multi_location_prep.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Optimized multi-location partially replicated design β€” multi_location_prep","text":"list class FielDHub several elements. infoDesign list information design parameters. layoutRandom matrix randomization layout. plotNumber matrix layout plot number. binaryField matrix binary field. dataEntry data frame data input. genEntries list entries replicated non-replicated parts. fieldBook data frame field book design. includes index (Row, Column). min_pairwise_distance data frame minimum pairwise distance pair locations. reps_info data frame information number replicated non-replicated treatments location. pairsDistance data frame pairwise distances pair treatments. treatments_with_reps list entries replicated part design. treatments_with_no_reps list entries non-replicated part design. list_locs list location list entries. allocation matrix allocation treatments. size_locations data frame one column location one row size location.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/multi_location_prep.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Optimized multi-location partially replicated design β€” multi_location_prep","text":"Edmondson, R.N. Multi-level Block Designs Comparative Experiments. JABES 25, 500–522 (2020). https://doi.org/10.1007/s13253-020-00416-0","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/multi_location_prep.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Optimized multi-location partially replicated design β€” multi_location_prep","text":"Didier Murillo [aut], Salvador Gezan [aut], Jean-Marc Montpetit [ctb], Ana Heilman [ctb]","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/multi_location_prep.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Optimized multi-location partially replicated design β€” multi_location_prep","text":"","code":"# Example 1: Generates a spatially optimized multi-location p-rep design with 142 # genotypes. The number of copies per plant available for this experiment is 9. # This experiment is carried out in 5 locations, and there are seven seeds available # for each plant to make replications. # In this case, we add three controls (checks) with six reps each. # With this setup, the experiment will have 142 treatments + 3 checks = 145 # entries and the number of plots per location after the allocation process # will be 196. # The average genotype allocation will be 1.5 copies per location. if (FALSE) { # \\dontrun{ optim_multi_prep <- multi_location_prep( lines = 150, l = 5, copies_per_entry = 7, checks = 3, rep_checks = c(6,6,6), locationNames = c(\"LOC1\", \"LOC2\", \"LOC3\", \"LOC4\", \"LOC5\"), seed = 1234 ) designs <- optim_multi_prep$designs field_book_loc_1 <- designs$LOC1$fieldBook head(field_book_loc_1, 10) } # }"},{"path":"https://didiermurillof.github.io/FielDHub/reference/optimized_arrangement.html","id":null,"dir":"Reference","previous_headings":"","what":"Generates an Spatial Un-replicated Optimized Arrangement Design β€” optimized_arrangement","title":"Generates an Spatial Un-replicated Optimized Arrangement Design β€” optimized_arrangement","text":"Randomly generates spatial un-replicated optimized arrangement design, distance checks maximized way row column control plots. Note design generation needs dimension field (number rows columns).","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/optimized_arrangement.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generates an Spatial Un-replicated Optimized Arrangement Design β€” optimized_arrangement","text":"","code":"optimized_arrangement( nrows = NULL, ncols = NULL, lines = NULL, amountChecks = NULL, checks = NULL, planter = \"serpentine\", l = 1, plotNumber = 101, seed = NULL, exptName = NULL, locationNames = NULL, optim = TRUE, data = NULL )"},{"path":"https://didiermurillof.github.io/FielDHub/reference/optimized_arrangement.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generates an Spatial Un-replicated Optimized Arrangement Design β€” optimized_arrangement","text":"nrows Number rows field. ncols Number columns field. lines Number genotypes, experimental lines treatments. amountChecks Integer amount total checks numeric vector replicates check label. checks Number genotypes checks. planter Option serpentine cartesian arrangement. default planter = 'serpentine'. l Number locations. default l = 1. plotNumber Numeric vector starting plot number location. default plotNumber = 101. seed (optional) Real number specifies starting seed obtain reproducible designs. exptName (optional) Name experiment. locationNames (optional) Name location. optim default optim = TRUE. data (optional) Data frame 3 columns: ENTRY | NAME | REPS.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/optimized_arrangement.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generates an Spatial Un-replicated Optimized Arrangement Design β€” optimized_arrangement","text":"list five elements. infoDesign list information design parameters. layoutRandom matrix randomization layout. plotNumber matrix layout plot number. dataEntry data frame data input. genEntries list entries replicated replicated part. fieldBook data frame field book design. includes index (Row, Column).","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/optimized_arrangement.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Generates an Spatial Un-replicated Optimized Arrangement Design β€” optimized_arrangement","text":"Clarke, G. P. Y., & Stefanova, K. T. (2011). Optimal design early-generation plant breeding trials unreplicated partially replicated test lines. Australian & New Zealand Journal Statistics, 53(4), 461–480.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/optimized_arrangement.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Generates an Spatial Un-replicated Optimized Arrangement Design β€” optimized_arrangement","text":"Didier Murillo [aut], Salvador Gezan [aut], Ana Heilman [ctb], Thomas Walk [ctb], Johan Aparicio [ctb], Richard Horsley [ctb]","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/optimized_arrangement.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generates an Spatial Un-replicated Optimized Arrangement Design β€” optimized_arrangement","text":"","code":"# Example 1: Generates a spatial unreplicated optimized arrangement design in one location # with 120 genotypes + 20 check plots (4 checks) for a field with dimension 14 rows x 10 cols. if (FALSE) { # \\dontrun{ optim_unrep1 <- optimized_arrangement( nrows = 14, ncols = 10, lines = 120, amountChecks = 20, checks = 1:4, planter = \"cartesian\", plotNumber = 101, exptName = \"20RW1\", locationNames = \"CASSELTON\", seed = 14124 ) optim_unrep1$infoDesign optim_unrep1$layoutRandom optim_unrep1$plotNumber head(optim_unrep1$fieldBook, 12) } # } # Example 2: Generates a spatial unreplicated optimized arrangement design in one location # with 200 genotypes + 20 check plots (4 checks) for a field with dimension 10 rows x 22 cols. # As example, we set up the data option with the entries list. if (FALSE) { # \\dontrun{ checks <- 4 list_checks <- paste(\"CH\", 1:checks, sep = \"\") treatments <- paste(\"G\", 5:204, sep = \"\") REPS <- c(5, 5, 5, 5, rep(1, 200)) treatment_list <- data.frame(list(ENTRY = 1:204, NAME = c(list_checks, treatments), REPS = REPS)) head(treatment_list, 12) tail(treatment_list, 12) optim_unrep2 <- optimized_arrangement( nrows = 10, ncols = 22, planter = \"serpentine\", plotNumber = 101, seed = 120, exptName = \"20YWA2\", locationNames = \"MINOT\", data = treatment_list ) optim_unrep2$infoDesign optim_unrep2$layoutRandom optim_unrep2$plotNumber head(optim_unrep2$fieldBook,12) } # }"},{"path":"https://didiermurillof.github.io/FielDHub/reference/partially_replicated.html","id":null,"dir":"Reference","previous_headings":"","what":"Generates a Spatial Partially Replicated Arrangement Design β€” partially_replicated","title":"Generates a Spatial Partially Replicated Arrangement Design β€” partially_replicated","text":"Randomly generates spatial partially replicated (p-rep) design single multiple locations.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/partially_replicated.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generates a Spatial Partially Replicated Arrangement Design β€” partially_replicated","text":"","code":"partially_replicated( nrows = NULL, ncols = NULL, repGens = NULL, repUnits = NULL, planter = \"serpentine\", l = 1, plotNumber = 101, seed = NULL, exptName = NULL, locationNames = NULL, multiLocationData = FALSE, data = NULL )"},{"path":"https://didiermurillof.github.io/FielDHub/reference/partially_replicated.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generates a Spatial Partially Replicated Arrangement Design β€” partially_replicated","text":"nrows Numeric vector number rows field location. ncols Numeric vector number columns field location. repGens Numeric vector amount genotypes replicate. repUnits Numeric vector number reps genotype. planter Option serpentine cartesian movement. default planter = 'serpentine'. l Number locations. default l = 1. plotNumber Numeric vector starting plot number location. default plotNumber = 101. seed (optional) Real number specifies starting seed obtain reproducible designs. exptName (optional) Name experiment. locationNames (optional) Name location. multiLocationData (optional) Option pass entry list multiple locations. default multiLocationData = FALSE. data (optional) Data frame 3 columns: ENTRY | NAME | REPS. multiLocationData = TRUE data must 4 columns: LOCATION | ENTRY | NAME | REPS","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/partially_replicated.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generates a Spatial Partially Replicated Arrangement Design β€” partially_replicated","text":"list several elements. infoDesign list information design parameters. layoutRandom matrix randomization layout. plotNumber matrix layout plot number. binaryField matrix binary field. dataEntry data frame data input. genEntries list entries replicated non-replicated parts. fieldBook data frame field book design. includes index (Row, Column). min_pairwise_distance data frame minimum pairwise distance pair locations. reps_info data frame information number replicated non-replicated treatments location. pairsDistance data frame pairwise distances pair treatments. treatments_with_reps list entries replicated part design. treatments_with_no_reps list entries non-replicated part design.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/partially_replicated.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generates a Spatial Partially Replicated Arrangement Design β€” partially_replicated","text":"function generates optimizes partially replicated (p-rep) experimental design given set treatments replication levels. design represented matrix optimized using pairwise distance metric. function outputs various information optimized design including field layout, replicated unreplicated treatments, pairwise distances treatments. Note design generation needs dimension field (number rows columns).","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/partially_replicated.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Generates a Spatial Partially Replicated Arrangement Design β€” partially_replicated","text":"Cullis, S., B. R., & Coombes, N. E. (2006). design early generation variety trials correlated data. Journal Agricultural, Biological, Environmental Statistics, 11, 381–393. https://doi.org/10.1198/108571106X154443","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/partially_replicated.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Generates a Spatial Partially Replicated Arrangement Design β€” partially_replicated","text":"Didier Murillo [aut], Salvador Gezan [aut], Ana Heilman [ctb], Thomas Walk [ctb], Johan Aparicio [ctb], Jean-Marc Montpetit [ctb], Richard Horsley [ctb]","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/partially_replicated.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generates a Spatial Partially Replicated Arrangement Design β€” partially_replicated","text":"","code":"# Example 1: Generates a spatial optimized partially replicated arrangement design in one # location with 335 genotypes for a field with dimensions 15 rows x 28 cols. # Note that there are 250 genotypes unreplicated (only one time), 85 genotypes replicated # two times, and three checks 8 times each. if (FALSE) { # \\dontrun{ prep_deseign1 <- partially_replicated( nrows = 12, ncols = 37, repGens = c(250, 85, 3), repUnits = c(1, 2, 8), planter = \"cartesian\", plotNumber = 101, seed = 77 ) prep_deseign1$infoDesign prep_deseign1$layoutRandom prep_deseign1$plotNumber head(prep_deseign1$fieldBook, 12) } # } # Example 2: Generates a spatial optimized partially replicated arrangement design with 492 # genotypes in a field with dimensions 30 rows x 20 cols. Note that there 384 genotypes # unreplicated (only one time), 108 genotypes replicated two times. # In this case we don't have check plots. # As example, we set up the data option with the entries list. if (FALSE) { # \\dontrun{ NAME <- paste(\"G\", 1:492, sep = \"\") repGens = c(108, 384);repUnits = c(2,1) REPS <- rep(repUnits, repGens) treatment_list <- data.frame(list(ENTRY = 1:492, NAME = NAME, REPS = REPS)) head(treatment_list, 12) tail(treatment_list, 12) prep_deseign2 <- partially_replicated( nrows = 30, ncols = 20, planter = \"serpentine\", plotNumber = 101, seed = 41, data = treatment_list ) prep_deseign2$infoDesign prep_deseign2$layoutRandom prep_deseign2$plotNumber head(prep_deseign2$fieldBook, 10) } # }"},{"path":"https://didiermurillof.github.io/FielDHub/reference/plot.FielDHub.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot a FielDHub object β€” plot.FielDHub","title":"Plot a FielDHub object β€” plot.FielDHub","text":"Draw field layout plot FielDHub object.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/plot.FielDHub.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot a FielDHub object β€” plot.FielDHub","text":"","code":"# S3 method for class 'FielDHub' plot(x, ...)"},{"path":"https://didiermurillof.github.io/FielDHub/reference/plot.FielDHub.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot a FielDHub object β€” plot.FielDHub","text":"x object inheriting class FielDHub ... arguments passed utility function plot_layout(). layout integer. Options available depend type design characteristics l integer specify location plot. planter can serpentine cartesian. stacked can vertical horizontal stacked layout.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/plot.FielDHub.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot a FielDHub object β€” plot.FielDHub","text":"plot object inheriting class fieldLayout field_book data frame fieldbook includes coordinates ROW COLUMN.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/plot.FielDHub.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot a FielDHub object β€” plot.FielDHub","text":"Didier Murillo [aut]","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/plot.FielDHub.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot a FielDHub object β€” plot.FielDHub","text":"","code":"if (FALSE) { # \\dontrun{ # Example 1: Plot a RCBD design with 24 treatments and 3 reps. s <- RCBD(t = 24, reps = 3, plotNumber = 101, seed = 12) plot(s) } # }"},{"path":"https://didiermurillof.github.io/FielDHub/reference/print.FielDHub.html","id":null,"dir":"Reference","previous_headings":"","what":"Print a FielDHub object β€” print.FielDHub","title":"Print a FielDHub object β€” print.FielDHub","text":"Prints information FielDHub function.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/print.FielDHub.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print a FielDHub object β€” print.FielDHub","text":"","code":"# S3 method for class 'FielDHub' print(x, n, ...)"},{"path":"https://didiermurillof.github.io/FielDHub/reference/print.FielDHub.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print a FielDHub object β€” print.FielDHub","text":"x object inheriting class n single integer. positive zero, size resulting object: number elements vector (including lists), rows matrix data frame lines function. negative, n last/first number elements x. ... arguments passed head.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/print.FielDHub.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print a FielDHub object β€” print.FielDHub","text":"object inheriting class FielDHub","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/print.FielDHub.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Print a FielDHub object β€” print.FielDHub","text":"Thiago de Paula Oliveira, thiago.paula.oliveira@alumni.usp.br [aut], Didier Murillo [aut]","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/print.FielDHub.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Print a FielDHub object β€” print.FielDHub","text":"","code":"# Example 1: Generates a CRD design with 5 treatments and 5 reps each. crd1 <- CRD(t = 5, reps = 5, plotNumber = 101, seed = 1985, locationName = \"Fargo\") crd1$infoDesign #> $numberofTreatments #> [1] 5 #> #> $treatments #> [1] \"T1\" \"T2\" \"T3\" \"T4\" \"T5\" #> #> $Reps #> [1] 5 #> #> $locationName #> [1] \"Fargo\" #> #> $seed #> [1] 1985 #> #> $id_design #> [1] 1 #> print(crd1) #> Completely Randomized Design (CRD) #> #> Information on the design parameters: #> List of 5 #> $ numberofTreatments: num 5 #> $ treatments : chr [1:5] \"T1\" \"T2\" \"T3\" \"T4\" ... #> $ Reps : num 5 #> $ locationName : chr \"Fargo\" #> $ seed : num 1985 #> #> 10 First observations of the data frame with the CRD field book: #> ID LOCATION PLOT REP TREATMENT #> 1 1 Fargo 101 3 T3 #> 2 2 Fargo 102 4 T2 #> 3 3 Fargo 103 2 T1 #> 4 4 Fargo 104 3 T5 #> 5 5 Fargo 105 2 T5 #> 6 6 Fargo 106 2 T4 #> 7 7 Fargo 107 4 T3 #> 8 8 Fargo 108 5 T4 #> 9 9 Fargo 109 1 T2 #> 10 10 Fargo 110 5 T1"},{"path":"https://didiermurillof.github.io/FielDHub/reference/print.fieldLayout.html","id":null,"dir":"Reference","previous_headings":"","what":"Print a fieldLayout plot object β€” print.fieldLayout","title":"Print a fieldLayout plot object β€” print.fieldLayout","text":"Prints plot object class fieldLayout.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/print.fieldLayout.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print a fieldLayout plot object β€” print.fieldLayout","text":"","code":"# S3 method for class 'fieldLayout' print(x, ...)"},{"path":"https://didiermurillof.github.io/FielDHub/reference/print.fieldLayout.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print a fieldLayout plot object β€” print.fieldLayout","text":"x plot object inheriting class fieldLayout. ... unused, extensibility.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/print.fieldLayout.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print a fieldLayout plot object β€” print.fieldLayout","text":"plot object inheriting class fieldLayout.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/print.fieldLayout.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Print a fieldLayout plot object β€” print.fieldLayout","text":"Didier Murillo [aut]","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/print.summary.FielDHub.html","id":null,"dir":"Reference","previous_headings":"","what":"Print the summary of a FielDHub object β€” print.summary.FielDHub","title":"Print the summary of a FielDHub object β€” print.summary.FielDHub","text":"Print summary information design parameters, data frame structure","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/print.summary.FielDHub.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print the summary of a FielDHub object β€” print.summary.FielDHub","text":"","code":"# S3 method for class 'summary.FielDHub' print(x, ...)"},{"path":"https://didiermurillof.github.io/FielDHub/reference/print.summary.FielDHub.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print the summary of a FielDHub object β€” print.summary.FielDHub","text":"x object inheriting class FielDHub ... Unused, extensibility","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/print.summary.FielDHub.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print the summary of a FielDHub object β€” print.summary.FielDHub","text":"object inheriting class FielDHub","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/print.summary.FielDHub.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Print the summary of a FielDHub object β€” print.summary.FielDHub","text":"Thiago de Paula Oliveira, thiago.paula.oliveira@alumni.usp.br [aut], Didier Murillo [aut]","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/rectangular_lattice.html","id":null,"dir":"Reference","previous_headings":"","what":"Generates a Rectangular Lattice Design. β€” rectangular_lattice","title":"Generates a Rectangular Lattice Design. β€” rectangular_lattice","text":"randomly generates rectangular lattice design across locations.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/rectangular_lattice.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generates a Rectangular Lattice Design. β€” rectangular_lattice","text":"","code":"rectangular_lattice( t = NULL, k = NULL, r = NULL, l = 1, plotNumber = 101, locationNames = NULL, seed = NULL, data = NULL )"},{"path":"https://didiermurillof.github.io/FielDHub/reference/rectangular_lattice.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generates a Rectangular Lattice Design. β€” rectangular_lattice","text":"t Number treatments. k Size incomplete blocks (number units per incomplete block). r Number blocks (full resolvable replicates). l Number locations. default l = 1. plotNumber Numeric vector starting plot number location. default plotNumber = 101. locationNames (optional) Names location. seed (optional) Real number specifies starting seed obtain reproducible designs. data (optional) Data frame label list treatments.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/rectangular_lattice.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generates a Rectangular Lattice Design. β€” rectangular_lattice","text":"list two elements. infoDesign list information design parameters. fieldBook data frame rectangular lattice design field book.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/rectangular_lattice.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Generates a Rectangular Lattice Design. β€” rectangular_lattice","text":"Edmondson., R. N. (2021). blocksdesign: Nested crossed block designs factorial unstructured treatment sets. https://CRAN.R-project.org/package=blocksdesign","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/rectangular_lattice.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Generates a Rectangular Lattice Design. β€” rectangular_lattice","text":"Didier Murillo [aut], Salvador Gezan [aut], Ana Heilman [ctb], Thomas Walk [ctb], Johan Aparicio [ctb], Richard Horsley [ctb]","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/rectangular_lattice.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generates a Rectangular Lattice Design. β€” rectangular_lattice","text":"","code":"# Example 1: Generates a rectangular lattice design with 6 full blocks, 4 units per IBlock (k) # and 20 treatments in one location. rectangularLattice1 <- rectangular_lattice(t = 20, k = 4, r = 6, l = 1, plotNumber = 101, locationNames = \"FARGO\", seed = 126) rectangularLattice1$infoDesign #> $Reps #> [1] 6 #> #> $iBlocks #> [1] 5 #> #> $NumberTreatments #> [1] 20 #> #> $NumberLocations #> [1] 1 #> #> $Locations #> [1] \"FARGO\" #> #> $seed #> [1] 126 #> #> $lambda #> [1] 0.9473684 #> #> $id_design #> [1] 11 #> head(rectangularLattice1$fieldBook,12) #> ID LOCATION PLOT REP IBLOCK UNIT ENTRY TREATMENT #> 1 1 FARGO 101 1 1 1 5 G-5 #> 2 2 FARGO 102 1 1 2 15 G-15 #> 3 3 FARGO 103 1 1 3 3 G-3 #> 4 4 FARGO 104 1 1 4 14 G-14 #> 5 5 FARGO 105 1 2 1 12 G-12 #> 6 6 FARGO 106 1 2 2 1 G-1 #> 7 7 FARGO 107 1 2 3 10 G-10 #> 8 8 FARGO 108 1 2 4 16 G-16 #> 9 9 FARGO 109 1 3 1 7 G-7 #> 10 10 FARGO 110 1 3 2 19 G-19 #> 11 11 FARGO 111 1 3 3 11 G-11 #> 12 12 FARGO 112 1 3 4 6 G-6 # Example 2: Generates a rectangular lattice design with 5 full blocks, 7 units per IBlock (k) # and 56 treatments across 2 locations. # In this case, we show how to use the option data. treatments <- paste(\"ND-\", 1:56, sep = \"\") ENTRY <- 1:56 treatment_list <- data.frame(list(ENTRY = ENTRY, TREATMENT = treatments)) head(treatment_list) #> ENTRY TREATMENT #> 1 1 ND-1 #> 2 2 ND-2 #> 3 3 ND-3 #> 4 4 ND-4 #> 5 5 ND-5 #> 6 6 ND-6 rectangularLattice2 <- rectangular_lattice(t = 56, k = 7, r = 5, l = 2, plotNumber = c(1001,2001), locationNames = c(\"Loc1\", \"Loc2\"), seed = 127, data = treatment_list) rectangularLattice2$infoDesign #> $Reps #> [1] 5 #> #> $iBlocks #> [1] 8 #> #> $NumberTreatments #> [1] 56 #> #> $NumberLocations #> [1] 2 #> #> $Locations #> [1] \"LOC1\" \"LOC2\" #> #> $seed #> [1] 127 #> #> $lambda #> [1] 0.5454545 #> #> $id_design #> [1] 11 #> head(rectangularLattice2$fieldBook,12) #> ID LOCATION PLOT REP IBLOCK UNIT ENTRY TREATMENT #> 1 1 LOC1 1001 1 1 1 43 ND-43 #> 2 2 LOC1 1002 1 1 2 6 ND-6 #> 3 3 LOC1 1003 1 1 3 5 ND-5 #> 4 4 LOC1 1004 1 1 4 27 ND-27 #> 5 5 LOC1 1005 1 1 5 54 ND-54 #> 6 6 LOC1 1006 1 1 6 41 ND-41 #> 7 7 LOC1 1007 1 1 7 26 ND-26 #> 8 8 LOC1 1008 1 2 1 24 ND-24 #> 9 9 LOC1 1009 1 2 2 51 ND-51 #> 10 10 LOC1 1010 1 2 3 21 ND-21 #> 11 11 LOC1 1011 1 2 4 20 ND-20 #> 12 12 LOC1 1012 1 2 5 11 ND-11"},{"path":"https://didiermurillof.github.io/FielDHub/reference/row_column.html","id":null,"dir":"Reference","previous_headings":"","what":"Generates a Resolvable Row-Column Design (RowColD) β€” row_column","title":"Generates a Resolvable Row-Column Design (RowColD) β€” row_column","text":"randomly generates resolvable row-column design (RowColD). design optimized rows columns blocking factors. randomization can done across multiple locations.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/row_column.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generates a Resolvable Row-Column Design (RowColD) β€” row_column","text":"","code":"row_column( t = NULL, nrows = NULL, r = NULL, l = 1, plotNumber = 101, locationNames = NULL, seed = NULL, iterations = 1000, data = NULL )"},{"path":"https://didiermurillof.github.io/FielDHub/reference/row_column.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generates a Resolvable Row-Column Design (RowColD) β€” row_column","text":"t Number treatments. nrows Number rows full resolvable replicate. r Number blocks (full resolvable replicates). l Number locations. default l = 1. plotNumber Numeric vector starting plot number location. default plotNumber = 101. locationNames (optional) Names location. seed (optional) Real number specifies starting seed obtain reproducible designs. iterations Number iterations design optimization. default iterations = 1000. data (optional) Data frame label list treatments","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/row_column.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generates a Resolvable Row-Column Design (RowColD) β€” row_column","text":"list four elements. infoDesign list information design parameters. resolvableBlocks list resolvable row columns blocks. concurrence concurrence matrix. fieldBook data frame row-column field book.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/row_column.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generates a Resolvable Row-Column Design (RowColD) β€” row_column","text":"Row-Column design FielDHub built two stages. first step constructs blocking factor Columns using Incomplete Block Units incomplete block design sets number incomplete blocks number Columns design, dimension equal number Rows. design generated, Rows used Row blocking factor optimized -Efficiency, levels within original Columns fixed. optimize Rows maintaining current optimized Columns, use heuristic algorithm swaps random treatment positions within given Column (Block) also selected random. algorithm begins calculating -Efficiency initial design, performs swap iteration, recalculates -Efficiency resulting design, compares previous one decide whether keep discard new design. iterative process repeated, default, 1,000 times.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/row_column.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Generates a Resolvable Row-Column Design (RowColD) β€” row_column","text":"Edmondson., R. N. (2021). blocksdesign: Nested crossed block designs factorial unstructured treatment sets. https://CRAN.R-project.org/package=blocksdesign","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/row_column.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Generates a Resolvable Row-Column Design (RowColD) β€” row_column","text":"Didier Murillo [aut], Salvador Gezan [aut], Ana Heilman [ctb], Thomas Walk [ctb], Johan Aparicio [ctb], Richard Horsley [ctb]","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/row_column.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generates a Resolvable Row-Column Design (RowColD) β€” row_column","text":"","code":"# Example 1: Generates a row-column design with 3 full blocks and 24 treatments # and 6 rows. This for one location. rowcold1 <- row_column(t = 24, nrows = 6, r = 3, l = 1, plotNumber= 101, locationNames = \"Loc1\", iterations = 500, seed = 21) rowcold1$infoDesign #> $rows #> [1] 6 #> #> $columns #> [1] 4 #> #> $reps #> [1] 3 #> #> $treatments #> [1] 24 #> #> $locations #> [1] 1 #> #> $location_names #> [1] \"Loc1\" #> #> $seed #> [1] 21 #> #> $id_design #> [1] 9 #> rowcold1$resolvableBlocks #> $Loc_Loc1 #> $Loc_Loc1$rep1 #> [,1] [,2] [,3] [,4] #> [1,] NA NA NA NA #> [2,] NA NA NA NA #> [3,] NA NA NA NA #> [4,] NA NA NA NA #> [5,] NA NA NA NA #> [6,] NA NA NA NA #> #> $Loc_Loc1$rep2 #> [,1] [,2] [,3] [,4] #> [1,] NA NA NA NA #> [2,] NA NA NA NA #> [3,] NA NA NA NA #> [4,] NA NA NA NA #> [5,] NA NA NA NA #> [6,] NA NA NA NA #> #> $Loc_Loc1$rep3 #> [,1] [,2] [,3] [,4] #> [1,] NA NA NA NA #> [2,] NA NA NA NA #> [3,] NA NA NA NA #> [4,] NA NA NA NA #> [5,] NA NA NA NA #> [6,] NA NA NA NA #> #> head(rowcold1$fieldBook,12) #> ID LOCATION PLOT REP ROW COLUMN ENTRY TREATMENT #> 1 1 Loc1 101 1 1 1 24 G-24 #> 7 7 Loc1 102 1 1 2 22 G-22 #> 13 13 Loc1 103 1 1 3 16 G-16 #> 19 19 Loc1 104 1 1 4 4 G-4 #> 2 2 Loc1 105 1 2 1 15 G-15 #> 8 8 Loc1 106 1 2 2 1 G-1 #> 14 14 Loc1 107 1 2 3 8 G-8 #> 20 20 Loc1 108 1 2 4 3 G-3 #> 3 3 Loc1 109 1 3 1 21 G-21 #> 9 9 Loc1 110 1 3 2 11 G-11 #> 15 15 Loc1 111 1 3 3 10 G-10 #> 21 21 Loc1 112 1 3 4 7 G-7 # Example 2: Generates a row-column design with 3 full blocks and 30 treatments # and 5 rows, for one location. # In this case, we show how to use the option data. treatments <- paste(\"ND-\", 1:30, sep = \"\") ENTRY <- 1:30 treatment_list <- data.frame(list(ENTRY = ENTRY, TREATMENT = treatments)) head(treatment_list) #> ENTRY TREATMENT #> 1 1 ND-1 #> 2 2 ND-2 #> 3 3 ND-3 #> 4 4 ND-4 #> 5 5 ND-5 #> 6 6 ND-6 rowcold2 <- row_column(t = 30, nrows = 5, r = 3, l = 1, plotNumber= c(101,1001), locationNames = c(\"A\", \"B\"), seed = 15, iterations = 500, data = treatment_list) #> Warning: Length of plot numbers is larger than number of locations. rowcold2$infoDesign #> $rows #> [1] 5 #> #> $columns #> [1] 6 #> #> $reps #> [1] 3 #> #> $treatments #> [1] 30 #> #> $locations #> [1] 1 #> #> $location_names #> [1] 1 #> #> $seed #> [1] 15 #> #> $id_design #> [1] 9 #> rowcold2$resolvableBlocks #> $Loc_1 #> $Loc_1$rep1 #> [,1] [,2] [,3] [,4] [,5] [,6] #> [1,] NA NA NA NA NA NA #> [2,] NA NA NA NA NA NA #> [3,] NA NA NA NA NA NA #> [4,] NA NA NA NA NA NA #> [5,] NA NA NA NA NA NA #> #> $Loc_1$rep2 #> [,1] [,2] [,3] [,4] [,5] [,6] #> [1,] NA NA NA NA NA NA #> [2,] NA NA NA NA NA NA #> [3,] NA NA NA NA NA NA #> [4,] NA NA NA NA NA NA #> [5,] NA NA NA NA NA NA #> #> $Loc_1$rep3 #> [,1] [,2] [,3] [,4] [,5] [,6] #> [1,] NA NA NA NA NA NA #> [2,] NA NA NA NA NA NA #> [3,] NA NA NA NA NA NA #> [4,] NA NA NA NA NA NA #> [5,] NA NA NA NA NA NA #> #> head(rowcold2$fieldBook,12) #> ID LOCATION PLOT REP ROW COLUMN ENTRY TREATMENT #> 1 1 1 101 1 1 1 26 ND-26 #> 6 6 1 102 1 1 2 8 ND-8 #> 11 11 1 103 1 1 3 9 ND-9 #> 16 16 1 104 1 1 4 18 ND-18 #> 21 21 1 105 1 1 5 19 ND-19 #> 26 26 1 106 1 1 6 21 ND-21 #> 2 2 1 107 1 2 1 11 ND-11 #> 7 7 1 108 1 2 2 15 ND-15 #> 12 12 1 109 1 2 3 22 ND-22 #> 17 17 1 110 1 2 4 24 ND-24 #> 22 22 1 111 1 2 5 7 ND-7 #> 27 27 1 112 1 2 6 28 ND-28"},{"path":"https://didiermurillof.github.io/FielDHub/reference/run_app.html","id":null,"dir":"Reference","previous_headings":"","what":"Run the Shiny Application β€” run_app","title":"Run the Shiny Application β€” run_app","text":"Run Shiny Application","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/run_app.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Run the Shiny Application β€” run_app","text":"","code":"run_app(...)"},{"path":"https://didiermurillof.github.io/FielDHub/reference/run_app.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Run the Shiny Application β€” run_app","text":"... Unused, extensibility","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/run_app.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Run the Shiny Application β€” run_app","text":"shiny app object","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/sparse_allocation.html","id":null,"dir":"Reference","previous_headings":"","what":"Unreplicated designs using the sparse allocation approach β€” sparse_allocation","title":"Unreplicated designs using the sparse allocation approach β€” sparse_allocation","text":"Unreplicated designs using sparse allocation approach","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/sparse_allocation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Unreplicated designs using the sparse allocation approach β€” sparse_allocation","text":"","code":"sparse_allocation( lines, nrows, ncols, l, planter = \"serpentine\", plotNumber, copies_per_entry, checks = NULL, exptName = NULL, locationNames, sparse_list, seed, data = NULL )"},{"path":"https://didiermurillof.github.io/FielDHub/reference/sparse_allocation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Unreplicated designs using the sparse allocation approach β€” sparse_allocation","text":"lines Number genotypes, experimental lines treatments. nrows Number rows field. ncols Number columns field. l Number locations sites. default l = 1. planter Option serpentine cartesian plot arrangement. default planter = 'serpentine'. plotNumber Numeric vector starting plot number location. default plotNumber = 101. copies_per_entry Number copies per plant. design sparse copies_per_entry < l checks Number genotypes checks. exptName (optional) Name experiment. locationNames (optional) Names location. sparse_list (optional) class \"Sparse\" object generated do_optim() function. seed (optional) Real number specifies starting seed obtain reproducible designs. data (optional) Data frame 2 columns: ENTRY | NAME . ENTRY must numeric.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/sparse_allocation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Unreplicated designs using the sparse allocation approach β€” sparse_allocation","text":"list four elements. designs list location unreplicated randomization. list_locs list location list entries. allocation matrix allocation treatments. size_locations data frame one column location one row size location.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/sparse_allocation.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Unreplicated designs using the sparse allocation approach β€” sparse_allocation","text":"Edmondson, R.N. Multi-level Block Designs Comparative Experiments. JABES 25, 500–522 (2020). https://doi.org/10.1007/s13253-020-00416-0","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/sparse_allocation.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Unreplicated designs using the sparse allocation approach β€” sparse_allocation","text":"Didier Murillo [aut], Salvador Gezan [aut], Ana Heilman [ctb]","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/sparse_allocation.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Unreplicated designs using the sparse allocation approach β€” sparse_allocation","text":"","code":"sparse <- sparse_allocation( lines = 120, l = 4, copies_per_entry = 3, checks = 4, locationNames = c(\"LOC1\", \"LOC2\", \"LOC3\", \"LOC4\", \"LOC5\"), seed = 1234 )"},{"path":"https://didiermurillof.github.io/FielDHub/reference/split_families.html","id":null,"dir":"Reference","previous_headings":"","what":"Split a population of genotypes randomly into several locations. β€” split_families","title":"Split a population of genotypes randomly into several locations. β€” split_families","text":"Split population genotypes randomly several locations, aim approximatelly number replicates genotype, line treatment per location.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/split_families.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Split a population of genotypes randomly into several locations. β€” split_families","text":"","code":"split_families(l = NULL, data = NULL)"},{"path":"https://didiermurillof.github.io/FielDHub/reference/split_families.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Split a population of genotypes randomly into several locations. β€” split_families","text":"l Number locations. data Data frame entry (ENTRY) labels treatment (NAME) number individuals per family group (FAMILY).","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/split_families.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Split a population of genotypes randomly into several locations. β€” split_families","text":"list two elements. rowsEachlist table summary cases. data_locations data frame entries location","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/split_families.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Split a population of genotypes randomly into several locations. β€” split_families","text":"Didier Murillo [aut], Salvador Gezan [aut], Ana Heilman [ctb], Thomas Walk [ctb], Johan Aparicio [ctb], Richard Horsley [ctb]","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/split_families.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Split a population of genotypes randomly into several locations. β€” split_families","text":"","code":"# Example 1: Split a population of 3000 and 200 families into 8 locations. # Original dataset is been simulated. set.seed(77) N <- 2000; families <- 100 ENTRY <- 1:N NAME <- paste0(\"SB-\", 1:N) FAMILY <- vector(mode = \"numeric\", length = N) x <- 1:N for (i in x) { FAMILY[i] <- sample(1:families, size = 1, replace = TRUE) } gen.list <- data.frame(list(ENTRY = ENTRY, NAME = NAME, FAMILY = FAMILY)) head(gen.list) #> ENTRY NAME FAMILY #> 1 1 SB-1 18 #> 2 2 SB-2 45 #> 3 3 SB-3 69 #> 4 4 SB-4 57 #> 5 5 SB-5 37 #> 6 6 SB-6 29 # Now we are going to use the split_families() function. split_population <- split_families(l = 8, data = gen.list) #> Error: object 'gen.list' not found print(split_population) #> Error in eval(expr, envir, enclos): object 'split_population' not found summary(split_population) #> Error in eval(expr, envir, enclos): object 'split_population' not found head(split_population$data_locations,12) #> Error in eval(expr, envir, enclos): object 'split_population' not found"},{"path":"https://didiermurillof.github.io/FielDHub/reference/split_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Generates a Split Plot Design β€” split_plot","title":"Generates a Split Plot Design β€” split_plot","text":"randomly generates split plot design (SPD) across locations.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/split_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generates a Split Plot Design β€” split_plot","text":"","code":"split_plot( wp = NULL, sp = NULL, reps = NULL, type = 2, l = 1, plotNumber = 101, seed = NULL, locationNames = NULL, factorLabels = TRUE, data = NULL )"},{"path":"https://didiermurillof.github.io/FielDHub/reference/split_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generates a Split Plot Design β€” split_plot","text":"wp Number whole plots, integer vector. sp Number sub plots per whole plot, integer vector. reps Number blocks (full replicates). type Option CRD RCBD designs. Values type = 1 (CRD) type = 2 (RCBD). default type = 2. l Number locations. default l = 1. plotNumber Numeric vector starting plot number location. default plotNumber = 101. seed (optional) Real number specifies starting seed obtain reproducible designs. locationNames (optional) Names location. factorLabels (optional) TRUE retain levels labels original data set otherwise, numeric labels assigned. Default factorLabels =TRUE. data (optional) Data frame label list treatments.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/split_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generates a Split Plot Design β€” split_plot","text":"list two elements. infoDesign list information design parameters. fieldBook data frame split plot field book.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/split_plot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Generates a Split Plot Design β€” split_plot","text":"Federer, W. T. (1955). Experimental Design. Theory Application. New York, USA. Macmillan Company.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/split_plot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Generates a Split Plot Design β€” split_plot","text":"Didier Murillo [aut], Salvador Gezan [aut], Ana Heilman [ctb], Thomas Walk [ctb], Johan Aparicio [ctb], Richard Horsley [ctb]","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/split_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generates a Split Plot Design β€” split_plot","text":"","code":"# Example 1: Generates a split plot design SPD with 4 whole plots, 2 sub plots per whole plot, # and 4 reps in an RCBD arrangement. This in for a single location. SPDExample1 <- split_plot(wp = 4, sp = 2, reps = 5, l = 1, plotNumber = 101, seed = 14, type = 2, locationNames = \"FARGO\") SPDExample1$infoDesign #> $WholePlots #> [1] 1 2 3 4 #> #> $SubPlots #> [1] 1 2 #> #> $locationNumber #> [1] 1 #> #> $locationNames #> [1] \"FARGO\" #> #> $plotNumbers #> [1] 101 #> #> $typeDesign #> [1] \"RCBD\" #> #> $seed #> [1] 14 #> #> $id_design #> [1] 5 #> SPDExample1$layoutlocations #> [[1]] #> PLOT REP Whole-plot Sub-plot #> [1,] \"101\" \"1\" \"1\" \"1 2\" #> [2,] \"102\" \"1\" \"4\" \"2 1\" #> [3,] \"103\" \"1\" \"3\" \"2 1\" #> [4,] \"104\" \"1\" \"2\" \"2 1\" #> [5,] \"201\" \"2\" \"3\" \"1 2\" #> [6,] \"202\" \"2\" \"2\" \"2 1\" #> [7,] \"203\" \"2\" \"4\" \"2 1\" #> [8,] \"204\" \"2\" \"1\" \"2 1\" #> [9,] \"301\" \"3\" \"4\" \"2 1\" #> [10,] \"302\" \"3\" \"2\" \"1 2\" #> [11,] \"303\" \"3\" \"1\" \"2 1\" #> [12,] \"304\" \"3\" \"3\" \"2 1\" #> [13,] \"401\" \"4\" \"1\" \"2 1\" #> [14,] \"402\" \"4\" \"3\" \"2 1\" #> [15,] \"403\" \"4\" \"2\" \"2 1\" #> [16,] \"404\" \"4\" \"4\" \"1 2\" #> [17,] \"501\" \"5\" \"3\" \"1 2\" #> [18,] \"502\" \"5\" \"1\" \"2 1\" #> [19,] \"503\" \"5\" \"2\" \"2 1\" #> [20,] \"504\" \"5\" \"4\" \"2 1\" #> head(SPDExample1$fieldBook,12) #> ID LOCATION PLOT REP WHOLE_PLOT SUB_PLOT TRT_COMB #> 1 1 FARGO 101 1 1 1 1|1 #> 2 2 FARGO 101 1 1 2 1|2 #> 3 3 FARGO 102 1 4 2 4|2 #> 4 4 FARGO 102 1 4 1 4|1 #> 5 5 FARGO 103 1 3 2 3|2 #> 6 6 FARGO 103 1 3 1 3|1 #> 7 7 FARGO 104 1 2 2 2|2 #> 8 8 FARGO 104 1 2 1 2|1 #> 9 9 FARGO 201 2 3 1 3|1 #> 10 10 FARGO 201 2 3 2 3|2 #> 11 11 FARGO 202 2 2 2 2|2 #> 12 12 FARGO 202 2 2 1 2|1 # Example 2: Generates a split plot design SPD with 5 whole plots # (4 types of fungicide + one control), 10 sub plots per whole plot (10 bean varieties), # and 6 reps in an RCBD arrangement. This in 3 locations or sites. # In this case, we show how to use the option data. wp <- c(\"NFung\", paste(\"Fung\", 1:4, sep = \"\")) # Fungicides (5 Whole plots) sp <- paste(\"Beans\", 1:10, sep = \"\") # Beans varieties (10 sub plots) split_plot_Data <- data.frame(list(WHOLPLOT = c(wp, rep(NA, 5)), SUBPLOT = sp)) head(split_plot_Data, 12) #> WHOLPLOT SUBPLOT #> 1 NFung Beans1 #> 2 Fung1 Beans2 #> 3 Fung2 Beans3 #> 4 Fung3 Beans4 #> 5 Fung4 Beans5 #> 6 Beans6 #> 7 Beans7 #> 8 Beans8 #> 9 Beans9 #> 10 Beans10 SPDExample2 <- split_plot(reps = 6, l = 3, plotNumber = c(101, 1001, 2001), seed = 23, type = 2, locationNames = c(\"A\", \"B\", \"C\"), data = split_plot_Data) SPDExample2$infoDesign #> $WholePlots #> [1] \"NFung\" \"Fung1\" \"Fung2\" \"Fung3\" \"Fung4\" #> #> $SubPlots #> [1] \"Beans1\" \"Beans2\" \"Beans3\" \"Beans4\" \"Beans5\" \"Beans6\" \"Beans7\" #> [8] \"Beans8\" \"Beans9\" \"Beans10\" #> #> $locationNumber #> [1] 3 #> #> $locationNames #> [1] \"A\" \"B\" \"C\" #> #> $plotNumbers #> [1] 101 1001 2001 #> #> $typeDesign #> [1] \"RCBD\" #> #> $seed #> [1] 23 #> #> $id_design #> [1] 5 #> SPDExample2$layoutlocations #> [[1]] #> PLOT REP Whole-plot #> [1,] \"101\" \"1\" \"Fung4\" #> [2,] \"102\" \"1\" \"Fung3\" #> [3,] \"103\" \"1\" \"Fung2\" #> [4,] \"104\" \"1\" \"Fung1\" #> [5,] \"105\" \"1\" \"NFung\" #> [6,] \"201\" \"2\" \"Fung2\" #> [7,] \"202\" \"2\" \"Fung4\" #> [8,] \"203\" \"2\" \"NFung\" #> [9,] \"204\" \"2\" \"Fung1\" #> [10,] \"205\" \"2\" \"Fung3\" #> [11,] \"301\" \"3\" \"NFung\" #> [12,] \"302\" \"3\" \"Fung2\" #> [13,] \"303\" \"3\" \"Fung4\" #> [14,] \"304\" \"3\" \"Fung3\" #> [15,] \"305\" \"3\" \"Fung1\" #> [16,] \"401\" \"4\" \"Fung3\" #> [17,] \"402\" \"4\" \"Fung2\" #> [18,] \"403\" \"4\" \"Fung1\" #> [19,] \"404\" \"4\" \"Fung4\" #> [20,] \"405\" \"4\" \"NFung\" #> [21,] \"501\" \"5\" \"Fung4\" #> [22,] \"502\" \"5\" \"Fung1\" #> [23,] \"503\" \"5\" \"Fung3\" #> [24,] \"504\" \"5\" \"NFung\" #> [25,] \"505\" \"5\" \"Fung2\" #> [26,] \"601\" \"6\" \"Fung1\" #> [27,] \"602\" \"6\" \"Fung2\" #> [28,] \"603\" \"6\" \"Fung3\" #> [29,] \"604\" \"6\" \"Fung4\" #> [30,] \"605\" \"6\" \"NFung\" #> Sub-plot #> [1,] \"Beans5 Beans1 Beans2 Beans3 Beans10 Beans6 Beans7 Beans9 Beans4 Beans8\" #> [2,] \"Beans9 Beans10 Beans8 Beans5 Beans7 Beans4 Beans2 Beans6 Beans3 Beans1\" #> [3,] \"Beans7 Beans10 Beans6 Beans2 Beans8 Beans3 Beans1 Beans4 Beans9 Beans5\" #> [4,] \"Beans7 Beans9 Beans8 Beans2 Beans3 Beans1 Beans5 Beans4 Beans6 Beans10\" #> [5,] \"Beans9 Beans10 Beans4 Beans3 Beans6 Beans7 Beans1 Beans2 Beans8 Beans5\" #> [6,] \"Beans4 Beans3 Beans9 Beans10 Beans1 Beans8 Beans5 Beans7 Beans6 Beans2\" #> [7,] \"Beans8 Beans7 Beans1 Beans5 Beans2 Beans10 Beans9 Beans6 Beans4 Beans3\" #> [8,] \"Beans8 Beans4 Beans1 Beans2 Beans9 Beans6 Beans3 Beans5 Beans10 Beans7\" #> [9,] \"Beans1 Beans7 Beans5 Beans4 Beans6 Beans9 Beans2 Beans8 Beans10 Beans3\" #> [10,] \"Beans6 Beans1 Beans4 Beans2 Beans7 Beans10 Beans3 Beans8 Beans9 Beans5\" #> [11,] \"Beans5 Beans3 Beans6 Beans9 Beans4 Beans1 Beans10 Beans7 Beans2 Beans8\" #> [12,] \"Beans3 Beans7 Beans4 Beans2 Beans8 Beans6 Beans5 Beans9 Beans10 Beans1\" #> [13,] \"Beans1 Beans6 Beans7 Beans9 Beans3 Beans2 Beans4 Beans5 Beans8 Beans10\" #> [14,] \"Beans8 Beans4 Beans2 Beans6 Beans10 Beans3 Beans5 Beans1 Beans9 Beans7\" #> [15,] \"Beans3 Beans8 Beans5 Beans1 Beans7 Beans10 Beans6 Beans2 Beans9 Beans4\" #> [16,] \"Beans4 Beans7 Beans5 Beans8 Beans9 Beans2 Beans10 Beans6 Beans1 Beans3\" #> [17,] \"Beans8 Beans9 Beans2 Beans1 Beans7 Beans6 Beans5 Beans10 Beans4 Beans3\" #> [18,] \"Beans9 Beans8 Beans2 Beans5 Beans1 Beans6 Beans10 Beans7 Beans4 Beans3\" #> [19,] \"Beans10 Beans3 Beans6 Beans1 Beans5 Beans8 Beans7 Beans2 Beans4 Beans9\" #> [20,] \"Beans6 Beans8 Beans10 Beans1 Beans7 Beans3 Beans5 Beans4 Beans2 Beans9\" #> [21,] \"Beans8 Beans7 Beans9 Beans6 Beans1 Beans5 Beans2 Beans3 Beans10 Beans4\" #> [22,] \"Beans3 Beans9 Beans8 Beans4 Beans1 Beans7 Beans10 Beans6 Beans2 Beans5\" #> [23,] \"Beans2 Beans5 Beans10 Beans1 Beans7 Beans6 Beans9 Beans4 Beans8 Beans3\" #> [24,] \"Beans8 Beans5 Beans7 Beans1 Beans9 Beans6 Beans2 Beans4 Beans3 Beans10\" #> [25,] \"Beans7 Beans8 Beans10 Beans4 Beans1 Beans9 Beans3 Beans2 Beans5 Beans6\" #> [26,] \"Beans3 Beans8 Beans4 Beans9 Beans2 Beans6 Beans1 Beans7 Beans10 Beans5\" #> [27,] \"Beans4 Beans10 Beans1 Beans8 Beans3 Beans9 Beans7 Beans5 Beans6 Beans2\" #> [28,] \"Beans5 Beans3 Beans6 Beans4 Beans2 Beans10 Beans8 Beans1 Beans9 Beans7\" #> [29,] \"Beans3 Beans7 Beans2 Beans5 Beans1 Beans9 Beans4 Beans10 Beans8 Beans6\" #> [30,] \"Beans8 Beans6 Beans2 Beans5 Beans9 Beans10 Beans1 Beans3 Beans4 Beans7\" #> #> [[2]] #> PLOT REP Whole-plot #> [1,] \"1001\" \"1\" \"Fung1\" #> [2,] \"1002\" \"1\" \"Fung3\" #> [3,] \"1003\" \"1\" \"NFung\" #> [4,] \"1004\" \"1\" \"Fung2\" #> [5,] \"1005\" \"1\" \"Fung4\" #> [6,] \"1101\" \"2\" \"Fung3\" #> [7,] \"1102\" \"2\" \"Fung2\" #> [8,] \"1103\" \"2\" \"Fung4\" #> [9,] \"1104\" \"2\" \"Fung1\" #> [10,] \"1105\" \"2\" \"NFung\" #> [11,] \"1201\" \"3\" \"NFung\" #> [12,] \"1202\" \"3\" \"Fung2\" #> [13,] \"1203\" \"3\" \"Fung1\" #> [14,] \"1204\" \"3\" \"Fung4\" #> [15,] \"1205\" \"3\" \"Fung3\" #> [16,] \"1301\" \"4\" \"Fung3\" #> [17,] \"1302\" \"4\" \"NFung\" #> [18,] \"1303\" \"4\" \"Fung2\" #> [19,] \"1304\" \"4\" \"Fung4\" #> [20,] \"1305\" \"4\" \"Fung1\" #> [21,] \"1401\" \"5\" \"Fung2\" #> [22,] \"1402\" \"5\" \"NFung\" #> [23,] \"1403\" \"5\" \"Fung1\" #> [24,] \"1404\" \"5\" \"Fung4\" #> [25,] \"1405\" \"5\" \"Fung3\" #> [26,] \"1501\" \"6\" \"Fung2\" #> [27,] \"1502\" \"6\" \"Fung1\" #> [28,] \"1503\" \"6\" \"NFung\" #> [29,] \"1504\" \"6\" \"Fung4\" #> [30,] \"1505\" \"6\" \"Fung3\" #> Sub-plot #> [1,] \"Beans3 Beans6 Beans8 Beans9 Beans4 Beans5 Beans7 Beans2 Beans1 Beans10\" #> [2,] \"Beans2 Beans4 Beans9 Beans10 Beans8 Beans3 Beans5 Beans6 Beans1 Beans7\" #> [3,] \"Beans3 Beans7 Beans1 Beans6 Beans5 Beans2 Beans4 Beans10 Beans8 Beans9\" #> [4,] \"Beans3 Beans5 Beans7 Beans6 Beans4 Beans10 Beans2 Beans9 Beans8 Beans1\" #> [5,] \"Beans4 Beans9 Beans8 Beans3 Beans6 Beans7 Beans5 Beans1 Beans2 Beans10\" #> [6,] \"Beans6 Beans3 Beans5 Beans2 Beans7 Beans10 Beans9 Beans1 Beans8 Beans4\" #> [7,] \"Beans8 Beans5 Beans6 Beans7 Beans10 Beans2 Beans3 Beans9 Beans4 Beans1\" #> [8,] \"Beans3 Beans1 Beans10 Beans4 Beans7 Beans9 Beans5 Beans2 Beans8 Beans6\" #> [9,] \"Beans7 Beans3 Beans9 Beans10 Beans1 Beans5 Beans6 Beans4 Beans8 Beans2\" #> [10,] \"Beans10 Beans1 Beans5 Beans9 Beans6 Beans3 Beans8 Beans7 Beans4 Beans2\" #> [11,] \"Beans6 Beans7 Beans8 Beans3 Beans5 Beans4 Beans2 Beans1 Beans9 Beans10\" #> [12,] \"Beans3 Beans9 Beans8 Beans5 Beans2 Beans1 Beans4 Beans6 Beans10 Beans7\" #> [13,] \"Beans2 Beans5 Beans9 Beans1 Beans8 Beans3 Beans4 Beans6 Beans10 Beans7\" #> [14,] \"Beans10 Beans7 Beans9 Beans8 Beans5 Beans1 Beans4 Beans3 Beans2 Beans6\" #> [15,] \"Beans1 Beans8 Beans2 Beans3 Beans7 Beans6 Beans5 Beans10 Beans4 Beans9\" #> [16,] \"Beans1 Beans4 Beans3 Beans9 Beans10 Beans5 Beans6 Beans7 Beans2 Beans8\" #> [17,] \"Beans1 Beans8 Beans6 Beans9 Beans7 Beans2 Beans3 Beans5 Beans10 Beans4\" #> [18,] \"Beans2 Beans3 Beans1 Beans8 Beans7 Beans6 Beans4 Beans9 Beans5 Beans10\" #> [19,] \"Beans9 Beans1 Beans10 Beans8 Beans7 Beans3 Beans5 Beans6 Beans4 Beans2\" #> [20,] \"Beans2 Beans1 Beans3 Beans7 Beans4 Beans10 Beans8 Beans6 Beans9 Beans5\" #> [21,] \"Beans10 Beans9 Beans6 Beans7 Beans4 Beans3 Beans5 Beans8 Beans1 Beans2\" #> [22,] \"Beans3 Beans10 Beans4 Beans7 Beans1 Beans8 Beans2 Beans9 Beans5 Beans6\" #> [23,] \"Beans8 Beans7 Beans2 Beans3 Beans10 Beans6 Beans5 Beans4 Beans1 Beans9\" #> [24,] \"Beans3 Beans10 Beans5 Beans8 Beans9 Beans4 Beans2 Beans1 Beans7 Beans6\" #> [25,] \"Beans2 Beans8 Beans4 Beans1 Beans5 Beans6 Beans7 Beans10 Beans3 Beans9\" #> [26,] \"Beans10 Beans1 Beans6 Beans2 Beans9 Beans8 Beans3 Beans5 Beans7 Beans4\" #> [27,] \"Beans4 Beans8 Beans7 Beans5 Beans10 Beans9 Beans2 Beans3 Beans1 Beans6\" #> [28,] \"Beans7 Beans9 Beans6 Beans5 Beans1 Beans8 Beans3 Beans10 Beans4 Beans2\" #> [29,] \"Beans3 Beans9 Beans8 Beans1 Beans7 Beans10 Beans6 Beans2 Beans4 Beans5\" #> [30,] \"Beans3 Beans2 Beans1 Beans8 Beans9 Beans6 Beans4 Beans7 Beans10 Beans5\" #> #> [[3]] #> PLOT REP Whole-plot #> [1,] \"2001\" \"1\" \"NFung\" #> [2,] \"2002\" \"1\" \"Fung2\" #> [3,] \"2003\" \"1\" \"Fung1\" #> [4,] \"2004\" \"1\" \"Fung4\" #> [5,] \"2005\" \"1\" \"Fung3\" #> [6,] \"2101\" \"2\" \"Fung1\" #> [7,] \"2102\" \"2\" \"Fung2\" #> [8,] \"2103\" \"2\" \"Fung4\" #> [9,] \"2104\" \"2\" \"NFung\" #> [10,] \"2105\" \"2\" \"Fung3\" #> [11,] \"2201\" \"3\" \"Fung3\" #> [12,] \"2202\" \"3\" \"NFung\" #> [13,] \"2203\" \"3\" \"Fung4\" #> [14,] \"2204\" \"3\" \"Fung2\" #> [15,] \"2205\" \"3\" \"Fung1\" #> [16,] \"2301\" \"4\" \"Fung3\" #> [17,] \"2302\" \"4\" \"Fung4\" #> [18,] \"2303\" \"4\" \"Fung1\" #> [19,] \"2304\" \"4\" \"Fung2\" #> [20,] \"2305\" \"4\" \"NFung\" #> [21,] \"2401\" \"5\" \"Fung3\" #> [22,] \"2402\" \"5\" \"Fung2\" #> [23,] \"2403\" \"5\" \"Fung1\" #> [24,] \"2404\" \"5\" \"Fung4\" #> [25,] \"2405\" \"5\" \"NFung\" #> [26,] \"2501\" \"6\" \"Fung1\" #> [27,] \"2502\" \"6\" \"Fung3\" #> [28,] \"2503\" \"6\" \"Fung2\" #> [29,] \"2504\" \"6\" \"Fung4\" #> [30,] \"2505\" \"6\" \"NFung\" #> Sub-plot #> [1,] \"Beans3 Beans2 Beans6 Beans4 Beans5 Beans10 Beans7 Beans1 Beans8 Beans9\" #> [2,] \"Beans5 Beans4 Beans9 Beans1 Beans6 Beans2 Beans10 Beans7 Beans8 Beans3\" #> [3,] \"Beans4 Beans7 Beans6 Beans1 Beans2 Beans10 Beans9 Beans3 Beans8 Beans5\" #> [4,] \"Beans9 Beans8 Beans3 Beans6 Beans1 Beans7 Beans10 Beans5 Beans2 Beans4\" #> [5,] \"Beans7 Beans10 Beans4 Beans8 Beans2 Beans5 Beans9 Beans1 Beans3 Beans6\" #> [6,] \"Beans8 Beans1 Beans3 Beans10 Beans6 Beans4 Beans9 Beans5 Beans7 Beans2\" #> [7,] \"Beans3 Beans6 Beans1 Beans5 Beans7 Beans10 Beans9 Beans4 Beans8 Beans2\" #> [8,] \"Beans4 Beans2 Beans7 Beans1 Beans10 Beans9 Beans6 Beans5 Beans3 Beans8\" #> [9,] \"Beans2 Beans7 Beans9 Beans3 Beans4 Beans1 Beans5 Beans6 Beans8 Beans10\" #> [10,] \"Beans9 Beans2 Beans4 Beans5 Beans6 Beans3 Beans1 Beans8 Beans10 Beans7\" #> [11,] \"Beans6 Beans10 Beans1 Beans7 Beans2 Beans9 Beans4 Beans5 Beans8 Beans3\" #> [12,] \"Beans5 Beans3 Beans10 Beans9 Beans4 Beans2 Beans7 Beans6 Beans1 Beans8\" #> [13,] \"Beans1 Beans2 Beans7 Beans8 Beans5 Beans3 Beans10 Beans6 Beans9 Beans4\" #> [14,] \"Beans5 Beans8 Beans1 Beans3 Beans10 Beans6 Beans2 Beans9 Beans4 Beans7\" #> [15,] \"Beans5 Beans8 Beans3 Beans4 Beans9 Beans1 Beans10 Beans7 Beans2 Beans6\" #> [16,] \"Beans9 Beans5 Beans8 Beans1 Beans3 Beans7 Beans2 Beans6 Beans10 Beans4\" #> [17,] \"Beans2 Beans5 Beans3 Beans9 Beans6 Beans7 Beans1 Beans4 Beans8 Beans10\" #> [18,] \"Beans3 Beans10 Beans1 Beans8 Beans2 Beans6 Beans9 Beans4 Beans5 Beans7\" #> [19,] \"Beans9 Beans10 Beans1 Beans7 Beans3 Beans6 Beans5 Beans4 Beans2 Beans8\" #> [20,] \"Beans9 Beans1 Beans4 Beans3 Beans6 Beans2 Beans5 Beans10 Beans8 Beans7\" #> [21,] \"Beans1 Beans9 Beans4 Beans8 Beans2 Beans5 Beans7 Beans6 Beans3 Beans10\" #> [22,] \"Beans4 Beans5 Beans10 Beans7 Beans6 Beans1 Beans2 Beans9 Beans8 Beans3\" #> [23,] \"Beans6 Beans3 Beans10 Beans8 Beans1 Beans9 Beans2 Beans4 Beans5 Beans7\" #> [24,] \"Beans6 Beans7 Beans8 Beans4 Beans3 Beans1 Beans9 Beans2 Beans5 Beans10\" #> [25,] \"Beans3 Beans9 Beans8 Beans7 Beans1 Beans5 Beans10 Beans2 Beans4 Beans6\" #> [26,] \"Beans8 Beans5 Beans7 Beans10 Beans1 Beans3 Beans2 Beans6 Beans4 Beans9\" #> [27,] \"Beans9 Beans4 Beans10 Beans7 Beans1 Beans6 Beans8 Beans5 Beans3 Beans2\" #> [28,] \"Beans7 Beans8 Beans1 Beans10 Beans3 Beans4 Beans2 Beans6 Beans9 Beans5\" #> [29,] \"Beans3 Beans5 Beans4 Beans2 Beans7 Beans10 Beans9 Beans8 Beans1 Beans6\" #> [30,] \"Beans3 Beans6 Beans2 Beans9 Beans1 Beans4 Beans8 Beans7 Beans5 Beans10\" #> head(SPDExample2$fieldBook,12) #> ID LOCATION PLOT REP WHOLE_PLOT SUB_PLOT TRT_COMB #> 1 1 A 101 1 Fung4 Beans5 Fung4|Beans5 #> 2 2 A 101 1 Fung4 Beans1 Fung4|Beans1 #> 3 3 A 101 1 Fung4 Beans2 Fung4|Beans2 #> 4 4 A 101 1 Fung4 Beans3 Fung4|Beans3 #> 5 5 A 101 1 Fung4 Beans10 Fung4|Beans10 #> 6 6 A 101 1 Fung4 Beans6 Fung4|Beans6 #> 7 7 A 101 1 Fung4 Beans7 Fung4|Beans7 #> 8 8 A 101 1 Fung4 Beans9 Fung4|Beans9 #> 9 9 A 101 1 Fung4 Beans4 Fung4|Beans4 #> 10 10 A 101 1 Fung4 Beans8 Fung4|Beans8 #> 11 11 A 102 1 Fung3 Beans9 Fung3|Beans9 #> 12 12 A 102 1 Fung3 Beans10 Fung3|Beans10"},{"path":"https://didiermurillof.github.io/FielDHub/reference/split_split_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Generates a Split Split Plot Design β€” split_split_plot","title":"Generates a Split Split Plot Design β€” split_split_plot","text":"randomly generates split split plot design (SSPD) across locations.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/split_split_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generates a Split Split Plot Design β€” split_split_plot","text":"","code":"split_split_plot( wp = NULL, sp = NULL, ssp = NULL, reps = NULL, type = 2, l = 1, plotNumber = 101, seed = NULL, locationNames = NULL, factorLabels = TRUE, data = NULL )"},{"path":"https://didiermurillof.github.io/FielDHub/reference/split_split_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generates a Split Split Plot Design β€” split_split_plot","text":"wp Number whole plots, integer vector. sp Number sub plots per whole plot, integer vector. ssp Number sub-sub plots, integer vector. reps Number blocks (full replicates). type Option CRD RCBD designs. Values type = 1 (CRD) type = 2 (RCBD). default type = 2. l Number locations. default l = 1. plotNumber Numeric vector starting plot number location. default plotNumber = 101. seed (optional) Real number specifies starting seed obtain reproducible designs. locationNames (optional) Names location. factorLabels (optional) TRUE retain levels labels original data set otherwise, numeric labels assigned. Default factorLabels =TRUE. data (optional) Data frame label list treatments.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/split_split_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generates a Split Split Plot Design β€” split_split_plot","text":"list two elements. infoDesign list information design parameters. fieldBook data frame split split plot field book.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/split_split_plot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Generates a Split Split Plot Design β€” split_split_plot","text":"Federer, W. T. (1955). Experimental Design. Theory Application. New York, USA. Macmillan Company.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/split_split_plot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Generates a Split Split Plot Design β€” split_split_plot","text":"Didier Murillo [aut], Salvador Gezan [aut], Ana Heilman [ctb], Thomas Walk [ctb], Johan Aparicio [ctb], Richard Horsley [ctb]","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/split_split_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generates a Split Split Plot Design β€” split_split_plot","text":"","code":"# Example 1: Generates a split split plot design SSPD with 5 whole plots, 2 sub-plots, # 3 sub-sub plots, and 3 reps in an RCBD arrangement. This is for one location. SSPD1 <- split_split_plot(wp = 4, sp = 2, ssp = 3, reps = 5, l = 1, plotNumber = 101, seed = 23, type = 2, locationNames = \"FARGO\") SSPD1$infoDesign #> $Whole.Plots #> [1] 1 2 3 4 #> #> $Sub.Plots #> [1] 1 2 #> #> $Sub.Sub.Plots #> [1] 1 2 3 #> #> $Locations #> [1] 1 #> #> $typeDesign #> [1] \"RCBD\" #> #> $seed #> [1] 23 #> #> $id_design #> [1] 6 #> head(SSPD1$fieldBook,12) #> ID LOCATION PLOT REP WHOLE_PLOT SUB_PLOT SUB_SUB_PLOT TRT_COMB #> 1 1 FARGO 101 1 1 2 2 1|2|2 #> 2 2 FARGO 101 1 1 2 1 1|2|1 #> 3 3 FARGO 101 1 1 2 3 1|2|3 #> 4 4 FARGO 101 1 1 1 2 1|1|2 #> 5 5 FARGO 101 1 1 1 1 1|1|1 #> 6 6 FARGO 101 1 1 1 3 1|1|3 #> 7 7 FARGO 102 1 3 1 2 3|1|2 #> 8 8 FARGO 102 1 3 1 1 3|1|1 #> 9 9 FARGO 102 1 3 1 3 3|1|3 #> 10 10 FARGO 102 1 3 2 2 3|2|2 #> 11 11 FARGO 102 1 3 2 1 3|2|1 #> 12 12 FARGO 102 1 3 2 3 3|2|3 # Example 2: Generates a split split plot design SSPD with 2 whole plost # (Irrigation, No irrigation), 5 sub plots (4 types of fungicide + one control), and # 10 sub-sub plots (Ten varieties of beans), and 4 reps in an RCBD arrangement. # This is for 3 locations. In this case, we show how to use the option data. wp <- paste(\"IRR_\", c(\"NO\", \"Yes\"), sep = \"\") #Irrigation (2 Whole plots) sp <- c(\"NFung\", paste(\"Fung\", 1:4, sep = \"\")) #Fungicides (5 Sub plots) ssp <- paste(\"Beans\", 1:10, sep = \"\") #Beans varieties (10 Sub-sub plots) split_split_plot_Data <- data.frame(list(WHOLPLOT = c(wp, rep(NA, 8)), SUBPLOT = c(sp, rep(NA, 5)), SUB_SUBPLOTS = ssp)) head(split_split_plot_Data, 10) #> WHOLPLOT SUBPLOT SUB_SUBPLOTS #> 1 IRR_NO NFung Beans1 #> 2 IRR_Yes Fung1 Beans2 #> 3 Fung2 Beans3 #> 4 Fung3 Beans4 #> 5 Fung4 Beans5 #> 6 Beans6 #> 7 Beans7 #> 8 Beans8 #> 9 Beans9 #> 10 Beans10 SSPD2 <- split_split_plot(reps = 4, l = 3, plotNumber = c(101, 1001, 2001), seed = 23, type = 2, locationNames = c(\"A\", \"B\", \"C\"), data = split_split_plot_Data) SSPD2$infoDesign #> $Whole.Plots #> [1] \"IRR_NO\" \"IRR_Yes\" #> #> $Sub.Plots #> [1] \"NFung\" \"Fung1\" \"Fung2\" \"Fung3\" \"Fung4\" #> #> $Sub.Sub.Plots #> [1] \"Beans1\" \"Beans2\" \"Beans3\" \"Beans4\" \"Beans5\" \"Beans6\" \"Beans7\" #> [8] \"Beans8\" \"Beans9\" \"Beans10\" #> #> $Locations #> [1] 3 #> #> $typeDesign #> [1] \"RCBD\" #> #> $seed #> [1] 23 #> #> $id_design #> [1] 6 #> head(SSPD2$fieldBook,12) #> ID LOCATION PLOT REP WHOLE_PLOT SUB_PLOT SUB_SUB_PLOT TRT_COMB #> 1 1 A 101 1 IRR_NO Fung3 Beans3 IRR_NO|Fung3|Beans3 #> 2 2 A 101 1 IRR_NO Fung3 Beans9 IRR_NO|Fung3|Beans9 #> 3 3 A 101 1 IRR_NO Fung3 Beans10 IRR_NO|Fung3|Beans10 #> 4 4 A 101 1 IRR_NO Fung3 Beans7 IRR_NO|Fung3|Beans7 #> 5 5 A 101 1 IRR_NO Fung3 Beans8 IRR_NO|Fung3|Beans8 #> 6 6 A 101 1 IRR_NO Fung3 Beans5 IRR_NO|Fung3|Beans5 #> 7 7 A 101 1 IRR_NO Fung3 Beans2 IRR_NO|Fung3|Beans2 #> 8 8 A 101 1 IRR_NO Fung3 Beans6 IRR_NO|Fung3|Beans6 #> 9 9 A 101 1 IRR_NO Fung3 Beans4 IRR_NO|Fung3|Beans4 #> 10 10 A 101 1 IRR_NO Fung3 Beans1 IRR_NO|Fung3|Beans1 #> 11 11 A 101 1 IRR_NO Fung1 Beans9 IRR_NO|Fung1|Beans9 #> 12 12 A 101 1 IRR_NO Fung1 Beans2 IRR_NO|Fung1|Beans2"},{"path":"https://didiermurillof.github.io/FielDHub/reference/square_lattice.html","id":null,"dir":"Reference","previous_headings":"","what":"Generates a Square Lattice Design. β€” square_lattice","title":"Generates a Square Lattice Design. β€” square_lattice","text":"randomly generates square lattice design across locations.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/square_lattice.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generates a Square Lattice Design. β€” square_lattice","text":"","code":"square_lattice( t = NULL, k = NULL, r = NULL, l = 1, plotNumber = 101, locationNames = NULL, seed = NULL, data = NULL )"},{"path":"https://didiermurillof.github.io/FielDHub/reference/square_lattice.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generates a Square Lattice Design. β€” square_lattice","text":"t Number treatments. k Size incomplete blocks (number units per incomplete block). r Number blocks (full resolvable replicates). l Number locations. default l = 1. plotNumber Numeric vector starting plot number location. default plotNumber = 101. locationNames (optional) Names location. seed (optional) Real number specifies starting seed obtain reproducible designs. data (optional) Data frame label list treatments.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/square_lattice.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generates a Square Lattice Design. β€” square_lattice","text":"list two elements. infoDesign list information design parameters. fieldBook data frame square lattice design field book.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/square_lattice.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Generates a Square Lattice Design. β€” square_lattice","text":"Edmondson., R. N. (2021). blocksdesign: Nested crossed block designs factorial unstructured treatment sets. https://CRAN.R-project.org/package=blocksdesign","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/square_lattice.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Generates a Square Lattice Design. β€” square_lattice","text":"Didier Murillo [aut], Salvador Gezan [aut], Ana Heilman [ctb], Thomas Walk [ctb], Johan Aparicio [ctb], Richard Horsley [ctb]","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/square_lattice.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generates a Square Lattice Design. β€” square_lattice","text":"","code":"# Example 1: Generates a square lattice design with 5 full blocks, 8 units per IBlock, # 8 IBlocks for a square number of treatmens of 64 in two locations. squareLattice1 <- square_lattice(t = 64, k = 8, r = 5, l = 2, plotNumber = c(1001, 2001), locationNames = c(\"FARGO\", \"MINOT\"), seed = 1986) squareLattice1$infoDesign #> $Reps #> [1] 5 #> #> $IBlocks #> [1] 8 #> #> $NumberTreatments #> [1] 64 #> #> $NumberLocations #> [1] 2 #> #> $Locations #> [1] \"FARGO\" \"MINOT\" #> #> $seed #> [1] 1986 #> #> $lambda #> [1] 0.5555556 #> #> $id_design #> [1] 10 #> head(squareLattice1$fieldBook,12) #> ID LOCATION PLOT REP IBLOCK UNIT ENTRY TREATMENT #> 1 1 FARGO 1001 1 1 1 43 G-43 #> 2 2 FARGO 1002 1 1 2 49 G-49 #> 3 3 FARGO 1003 1 1 3 35 G-35 #> 4 4 FARGO 1004 1 1 4 15 G-15 #> 5 5 FARGO 1005 1 1 5 45 G-45 #> 6 6 FARGO 1006 1 1 6 42 G-42 #> 7 7 FARGO 1007 1 1 7 40 G-40 #> 8 8 FARGO 1008 1 1 8 10 G-10 #> 9 9 FARGO 1009 1 2 1 61 G-61 #> 10 10 FARGO 1010 1 2 2 21 G-21 #> 11 11 FARGO 1011 1 2 3 62 G-62 #> 12 12 FARGO 1012 1 2 4 34 G-34 # Example 2: Generates a square lattice design with 3 full blocks, 7 units per IBlock, # 7 IBlocks for a square number of treatmens of 49 in one location. # In this case, we show how to use the option data. treatments <- paste(\"G\", 1:49, sep = \"\") ENTRY <- 1:49 treatment_list <- data.frame(list(ENTRY = ENTRY, TREATMENT = treatments)) head(treatment_list) #> ENTRY TREATMENT #> 1 1 G1 #> 2 2 G2 #> 3 3 G3 #> 4 4 G4 #> 5 5 G5 #> 6 6 G6 squareLattice2 <- square_lattice(t = 49, k = 7, r = 3, l = 1, plotNumber = 1001, locationNames = \"CASSELTON\", seed = 1986, data = treatment_list) squareLattice2$infoDesign #> $Reps #> [1] 3 #> #> $IBlocks #> [1] 7 #> #> $NumberTreatments #> [1] 49 #> #> $NumberLocations #> [1] 1 #> #> $Locations #> [1] \"CASSELTON\" #> #> $seed #> [1] 1986 #> #> $lambda #> [1] 0.375 #> #> $id_design #> [1] 10 #> head(squareLattice2$fieldBook,12) #> ID LOCATION PLOT REP IBLOCK UNIT ENTRY TREATMENT #> 1 1 CASSELTON 1001 1 1 1 27 G27 #> 2 2 CASSELTON 1002 1 1 2 30 G30 #> 3 3 CASSELTON 1003 1 1 3 42 G42 #> 4 4 CASSELTON 1004 1 1 4 1 G1 #> 5 5 CASSELTON 1005 1 1 5 20 G20 #> 6 6 CASSELTON 1006 1 1 6 26 G26 #> 7 7 CASSELTON 1007 1 1 7 48 G48 #> 8 8 CASSELTON 1008 1 2 1 49 G49 #> 9 9 CASSELTON 1009 1 2 2 29 G29 #> 10 10 CASSELTON 1010 1 2 3 24 G24 #> 11 11 CASSELTON 1011 1 2 4 34 G34 #> 12 12 CASSELTON 1012 1 2 5 47 G47"},{"path":"https://didiermurillof.github.io/FielDHub/reference/strip_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Strip Plot Design β€” strip_plot","title":"Strip Plot Design β€” strip_plot","text":"randomly generates strip plot design across locations.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/strip_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Strip Plot Design β€” strip_plot","text":"","code":"strip_plot( Hplots = NULL, Vplots = NULL, b = 1, l = 1, plotNumber = NULL, planter = \"serpentine\", locationNames = NULL, seed = NULL, factorLabels = TRUE, data = NULL )"},{"path":"https://didiermurillof.github.io/FielDHub/reference/strip_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Strip Plot Design β€” strip_plot","text":"Hplots Number horizontal factors, integer vector. Vplots Number vertical factors, integer vector. b Number blocks (full replicates). l Number locations. default l = 1. plotNumber Numeric vector starting plot number location. default plotNumber = 101. planter Option serpentine cartesian arrangement. default planter = 'serpentine'. locationNames (optional) Names location. seed (optional) Real number specifies starting seed obtain reproducible designs. factorLabels (optional) TRUE retain levels labels original data set otherwise, numeric labels assigned. Default factorLabels =TRUE. data (optional) data frame labels vertical hirizontal plots.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/strip_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Strip Plot Design β€” strip_plot","text":"list four elements. infoDesign list information design parameters. stripsBlockLoc list strip blocks location. plotLayouts list layout plot numbers location. fieldBook data frame strip plot field book.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/strip_plot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Strip Plot Design β€” strip_plot","text":"Federer, W. T. (1955). Experimental Design. Theory Application. New York, USA. Macmillan Company.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/strip_plot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Strip Plot Design β€” strip_plot","text":"Didier Murillo [aut], Salvador Gezan [aut], Ana Heilman [ctb], Thomas Walk [ctb], Johan Aparicio [ctb], Richard Horsley [ctb]","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/strip_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Strip Plot Design β€” strip_plot","text":"","code":"# Example 1: Generates a strip plot design with 5 vertical strips and 4 horizontal strips, # with 3 reps in one location. H <- paste(\"H\", 1:4, sep = \"\") V <- paste(\"V\", 1:5, sep = \"\") strip1 <- strip_plot(Hplots = H, Vplots = V, b = 3, l = 1, plotNumber = 101, planter = \"serpentine\", locationNames = \"A\", seed = 333) strip1$infoDesign #> $Hplots #> [1] 4 #> #> $Vplots #> [1] 5 #> #> $blocks #> [1] 3 #> #> $numberLocations #> [1] 1 #> #> $nameLocations #> [1] \"A\" #> #> $seed #> [1] 333 #> #> $id_design #> [1] 7 #> strip1$stripsBlockLoc #> $Loc_A #> $Loc_A$rep1 #> V4 V2 V5 V1 V3 #> H2 \"H2|V4\" \"H2|V2\" \"H2|V5\" \"H2|V1\" \"H2|V3\" #> H1 \"H1|V4\" \"H1|V2\" \"H1|V5\" \"H1|V1\" \"H1|V3\" #> H3 \"H3|V4\" \"H3|V2\" \"H3|V5\" \"H3|V1\" \"H3|V3\" #> H4 \"H4|V4\" \"H4|V2\" \"H4|V5\" \"H4|V1\" \"H4|V3\" #> #> $Loc_A$rep2 #> V1 V3 V4 V2 V5 #> H3 \"H3|V1\" \"H3|V3\" \"H3|V4\" \"H3|V2\" \"H3|V5\" #> H4 \"H4|V1\" \"H4|V3\" \"H4|V4\" \"H4|V2\" \"H4|V5\" #> H2 \"H2|V1\" \"H2|V3\" \"H2|V4\" \"H2|V2\" \"H2|V5\" #> H1 \"H1|V1\" \"H1|V3\" \"H1|V4\" \"H1|V2\" \"H1|V5\" #> #> $Loc_A$rep3 #> V3 V1 V2 V4 V5 #> H2 \"H2|V3\" \"H2|V1\" \"H2|V2\" \"H2|V4\" \"H2|V5\" #> H1 \"H1|V3\" \"H1|V1\" \"H1|V2\" \"H1|V4\" \"H1|V5\" #> H4 \"H4|V3\" \"H4|V1\" \"H4|V2\" \"H4|V4\" \"H4|V5\" #> H3 \"H3|V3\" \"H3|V1\" \"H3|V2\" \"H3|V4\" \"H3|V5\" #> #> strip1$plotLayouts #> $Loc_A #> $Loc_A$rep1 #> [,1] [,2] [,3] [,4] [,5] #> [1,] 101 102 103 104 105 #> [2,] 110 109 108 107 106 #> [3,] 111 112 113 114 115 #> [4,] 120 119 118 117 116 #> #> $Loc_A$rep2 #> [,1] [,2] [,3] [,4] [,5] #> [1,] 201 202 203 204 205 #> [2,] 210 209 208 207 206 #> [3,] 211 212 213 214 215 #> [4,] 220 219 218 217 216 #> #> $Loc_A$rep3 #> [,1] [,2] [,3] [,4] [,5] #> [1,] 301 302 303 304 305 #> [2,] 310 309 308 307 306 #> [3,] 311 312 313 314 315 #> [4,] 320 319 318 317 316 #> #> head(strip1$fieldBook,12) #> ID LOCATION PLOT REP HSTRIP VSTRIP TRT_COMB #> 1 1 A 101 1 H2 V4 H2|V4 #> 2 2 A 102 1 H2 V2 H2|V2 #> 3 3 A 103 1 H2 V5 H2|V5 #> 4 4 A 104 1 H2 V1 H2|V1 #> 5 5 A 105 1 H2 V3 H2|V3 #> 6 6 A 110 1 H1 V4 H1|V4 #> 7 7 A 109 1 H1 V2 H1|V2 #> 8 8 A 108 1 H1 V5 H1|V5 #> 9 9 A 107 1 H1 V1 H1|V1 #> 10 10 A 106 1 H1 V3 H1|V3 #> 11 11 A 111 1 H3 V4 H3|V4 #> 12 12 A 112 1 H3 V2 H3|V2 # Example 2: Generates a strip plot design with 5 vertical strips and 5 horizontal strips, # with 6 reps across to 3 locations. In this case, we show how to use the option data. Hplots <- LETTERS[1:5] Vplots <- LETTERS[1:4] strip_data <- data.frame(list(HPLOTS = Hplots, VPLOTS = c(Vplots, NA))) head(strip_data) #> HPLOTS VPLOTS #> 1 A A #> 2 B B #> 3 C C #> 4 D D #> 5 E strip2 <- strip_plot(Hplots = 5, Vplots = 5, b = 6, l = 3, plotNumber = c(101,1001,2001), planter = \"cartesian\", locationNames = c(\"A\", \"B\", \"C\"), seed = 222, data = strip_data) strip2$infoDesign #> $Hplots #> [1] 5 #> #> $Vplots #> [1] 4 #> #> $blocks #> [1] 6 #> #> $numberLocations #> [1] 3 #> #> $nameLocations #> [1] \"A\" \"B\" \"C\" #> #> $seed #> [1] 222 #> #> $id_design #> [1] 7 #> strip2$stripsBlockLoc #> $Loc_A #> $Loc_A$rep1 #> D B C A #> E \"E|D\" \"E|B\" \"E|C\" \"E|A\" #> B \"B|D\" \"B|B\" \"B|C\" \"B|A\" #> C \"C|D\" \"C|B\" \"C|C\" \"C|A\" #> D \"D|D\" \"D|B\" \"D|C\" \"D|A\" #> A \"A|D\" \"A|B\" \"A|C\" \"A|A\" #> #> $Loc_A$rep2 #> D B C A #> A \"A|D\" \"A|B\" \"A|C\" \"A|A\" #> B \"B|D\" \"B|B\" \"B|C\" \"B|A\" #> E \"E|D\" \"E|B\" \"E|C\" \"E|A\" #> D \"D|D\" \"D|B\" \"D|C\" \"D|A\" #> C \"C|D\" \"C|B\" \"C|C\" \"C|A\" #> #> $Loc_A$rep3 #> A D C B #> A \"A|A\" \"A|D\" \"A|C\" \"A|B\" #> D \"D|A\" \"D|D\" \"D|C\" \"D|B\" #> E \"E|A\" \"E|D\" \"E|C\" \"E|B\" #> B \"B|A\" \"B|D\" \"B|C\" \"B|B\" #> C \"C|A\" \"C|D\" \"C|C\" \"C|B\" #> #> $Loc_A$rep4 #> A B C D #> A \"A|A\" \"A|B\" \"A|C\" \"A|D\" #> C \"C|A\" \"C|B\" \"C|C\" \"C|D\" #> E \"E|A\" \"E|B\" \"E|C\" \"E|D\" #> B \"B|A\" \"B|B\" \"B|C\" \"B|D\" #> D \"D|A\" \"D|B\" \"D|C\" \"D|D\" #> #> $Loc_A$rep5 #> A C D B #> B \"B|A\" \"B|C\" \"B|D\" \"B|B\" #> C \"C|A\" \"C|C\" \"C|D\" \"C|B\" #> E \"E|A\" \"E|C\" \"E|D\" \"E|B\" #> A \"A|A\" \"A|C\" \"A|D\" \"A|B\" #> D \"D|A\" \"D|C\" \"D|D\" \"D|B\" #> #> $Loc_A$rep6 #> B C D A #> D \"D|B\" \"D|C\" \"D|D\" \"D|A\" #> E \"E|B\" \"E|C\" \"E|D\" \"E|A\" #> B \"B|B\" \"B|C\" \"B|D\" \"B|A\" #> C \"C|B\" \"C|C\" \"C|D\" \"C|A\" #> A \"A|B\" \"A|C\" \"A|D\" \"A|A\" #> #> #> $Loc_B #> $Loc_B$rep1 #> B C D A #> B \"B|B\" \"B|C\" \"B|D\" \"B|A\" #> D \"D|B\" \"D|C\" \"D|D\" \"D|A\" #> E \"E|B\" \"E|C\" \"E|D\" \"E|A\" #> A \"A|B\" \"A|C\" \"A|D\" \"A|A\" #> C \"C|B\" \"C|C\" \"C|D\" \"C|A\" #> #> $Loc_B$rep2 #> D C A B #> D \"D|D\" \"D|C\" \"D|A\" \"D|B\" #> A \"A|D\" \"A|C\" \"A|A\" \"A|B\" #> C \"C|D\" \"C|C\" \"C|A\" \"C|B\" #> E \"E|D\" \"E|C\" \"E|A\" \"E|B\" #> B \"B|D\" \"B|C\" \"B|A\" \"B|B\" #> #> $Loc_B$rep3 #> D A C B #> B \"B|D\" \"B|A\" \"B|C\" \"B|B\" #> E \"E|D\" \"E|A\" \"E|C\" \"E|B\" #> C \"C|D\" \"C|A\" \"C|C\" \"C|B\" #> D \"D|D\" \"D|A\" \"D|C\" \"D|B\" #> A \"A|D\" \"A|A\" \"A|C\" \"A|B\" #> #> $Loc_B$rep4 #> C D A B #> D \"D|C\" \"D|D\" \"D|A\" \"D|B\" #> C \"C|C\" \"C|D\" \"C|A\" \"C|B\" #> E \"E|C\" \"E|D\" \"E|A\" \"E|B\" #> A \"A|C\" \"A|D\" \"A|A\" \"A|B\" #> B \"B|C\" \"B|D\" \"B|A\" \"B|B\" #> #> $Loc_B$rep5 #> A B D C #> D \"D|A\" \"D|B\" \"D|D\" \"D|C\" #> C \"C|A\" \"C|B\" \"C|D\" \"C|C\" #> A \"A|A\" \"A|B\" \"A|D\" \"A|C\" #> B \"B|A\" \"B|B\" \"B|D\" \"B|C\" #> E \"E|A\" \"E|B\" \"E|D\" \"E|C\" #> #> $Loc_B$rep6 #> C D B A #> B \"B|C\" \"B|D\" \"B|B\" \"B|A\" #> D \"D|C\" \"D|D\" \"D|B\" \"D|A\" #> A \"A|C\" \"A|D\" \"A|B\" \"A|A\" #> C \"C|C\" \"C|D\" \"C|B\" \"C|A\" #> E \"E|C\" \"E|D\" \"E|B\" \"E|A\" #> #> #> $Loc_C #> $Loc_C$rep1 #> D A C B #> D \"D|D\" \"D|A\" \"D|C\" \"D|B\" #> B \"B|D\" \"B|A\" \"B|C\" \"B|B\" #> E \"E|D\" \"E|A\" \"E|C\" \"E|B\" #> A \"A|D\" \"A|A\" \"A|C\" \"A|B\" #> C \"C|D\" \"C|A\" \"C|C\" \"C|B\" #> #> $Loc_C$rep2 #> B C A D #> B \"B|B\" \"B|C\" \"B|A\" \"B|D\" #> A \"A|B\" \"A|C\" \"A|A\" \"A|D\" #> D \"D|B\" \"D|C\" \"D|A\" \"D|D\" #> C \"C|B\" \"C|C\" \"C|A\" \"C|D\" #> E \"E|B\" \"E|C\" \"E|A\" \"E|D\" #> #> $Loc_C$rep3 #> C D A B #> E \"E|C\" \"E|D\" \"E|A\" \"E|B\" #> C \"C|C\" \"C|D\" \"C|A\" \"C|B\" #> D \"D|C\" \"D|D\" \"D|A\" \"D|B\" #> A \"A|C\" \"A|D\" \"A|A\" \"A|B\" #> B \"B|C\" \"B|D\" \"B|A\" \"B|B\" #> #> $Loc_C$rep4 #> C D B A #> D \"D|C\" \"D|D\" \"D|B\" \"D|A\" #> A \"A|C\" \"A|D\" \"A|B\" \"A|A\" #> B \"B|C\" \"B|D\" \"B|B\" \"B|A\" #> E \"E|C\" \"E|D\" \"E|B\" \"E|A\" #> C \"C|C\" \"C|D\" \"C|B\" \"C|A\" #> #> $Loc_C$rep5 #> A B D C #> B \"B|A\" \"B|B\" \"B|D\" \"B|C\" #> D \"D|A\" \"D|B\" \"D|D\" \"D|C\" #> A \"A|A\" \"A|B\" \"A|D\" \"A|C\" #> E \"E|A\" \"E|B\" \"E|D\" \"E|C\" #> C \"C|A\" \"C|B\" \"C|D\" \"C|C\" #> #> $Loc_C$rep6 #> C D A B #> B \"B|C\" \"B|D\" \"B|A\" \"B|B\" #> E \"E|C\" \"E|D\" \"E|A\" \"E|B\" #> A \"A|C\" \"A|D\" \"A|A\" \"A|B\" #> D \"D|C\" \"D|D\" \"D|A\" \"D|B\" #> C \"C|C\" \"C|D\" \"C|A\" \"C|B\" #> #> strip2$plotLayouts #> $Loc_A #> $Loc_A$rep1 #> [,1] [,2] [,3] [,4] #> [1,] 101 102 103 104 #> [2,] 105 106 107 108 #> [3,] 109 110 111 112 #> [4,] 113 114 115 116 #> [5,] 117 118 119 120 #> #> $Loc_A$rep2 #> [,1] [,2] [,3] [,4] #> [1,] 201 202 203 204 #> [2,] 205 206 207 208 #> [3,] 209 210 211 212 #> [4,] 213 214 215 216 #> [5,] 217 218 219 220 #> #> $Loc_A$rep3 #> [,1] [,2] [,3] [,4] #> [1,] 301 302 303 304 #> [2,] 305 306 307 308 #> [3,] 309 310 311 312 #> [4,] 313 314 315 316 #> [5,] 317 318 319 320 #> #> $Loc_A$rep4 #> [,1] [,2] [,3] [,4] #> [1,] 401 402 403 404 #> [2,] 405 406 407 408 #> [3,] 409 410 411 412 #> [4,] 413 414 415 416 #> [5,] 417 418 419 420 #> #> $Loc_A$rep5 #> [,1] [,2] [,3] [,4] #> [1,] 501 502 503 504 #> [2,] 505 506 507 508 #> [3,] 509 510 511 512 #> [4,] 513 514 515 516 #> [5,] 517 518 519 520 #> #> $Loc_A$rep6 #> [,1] [,2] [,3] [,4] #> [1,] 601 602 603 604 #> [2,] 605 606 607 608 #> [3,] 609 610 611 612 #> [4,] 613 614 615 616 #> [5,] 617 618 619 620 #> #> #> $Loc_B #> $Loc_B$rep1 #> [,1] [,2] [,3] [,4] #> [1,] 1001 1002 1003 1004 #> [2,] 1005 1006 1007 1008 #> [3,] 1009 1010 1011 1012 #> [4,] 1013 1014 1015 1016 #> [5,] 1017 1018 1019 1020 #> #> $Loc_B$rep2 #> [,1] [,2] [,3] [,4] #> [1,] 1101 1102 1103 1104 #> [2,] 1105 1106 1107 1108 #> [3,] 1109 1110 1111 1112 #> [4,] 1113 1114 1115 1116 #> [5,] 1117 1118 1119 1120 #> #> $Loc_B$rep3 #> [,1] [,2] [,3] [,4] #> [1,] 1201 1202 1203 1204 #> [2,] 1205 1206 1207 1208 #> [3,] 1209 1210 1211 1212 #> [4,] 1213 1214 1215 1216 #> [5,] 1217 1218 1219 1220 #> #> $Loc_B$rep4 #> [,1] [,2] [,3] [,4] #> [1,] 1301 1302 1303 1304 #> [2,] 1305 1306 1307 1308 #> [3,] 1309 1310 1311 1312 #> [4,] 1313 1314 1315 1316 #> [5,] 1317 1318 1319 1320 #> #> $Loc_B$rep5 #> [,1] [,2] [,3] [,4] #> [1,] 1401 1402 1403 1404 #> [2,] 1405 1406 1407 1408 #> [3,] 1409 1410 1411 1412 #> [4,] 1413 1414 1415 1416 #> [5,] 1417 1418 1419 1420 #> #> $Loc_B$rep6 #> [,1] [,2] [,3] [,4] #> [1,] 1501 1502 1503 1504 #> [2,] 1505 1506 1507 1508 #> [3,] 1509 1510 1511 1512 #> [4,] 1513 1514 1515 1516 #> [5,] 1517 1518 1519 1520 #> #> #> $Loc_C #> $Loc_C$rep1 #> [,1] [,2] [,3] [,4] #> [1,] 2001 2002 2003 2004 #> [2,] 2005 2006 2007 2008 #> [3,] 2009 2010 2011 2012 #> [4,] 2013 2014 2015 2016 #> [5,] 2017 2018 2019 2020 #> #> $Loc_C$rep2 #> [,1] [,2] [,3] [,4] #> [1,] 2101 2102 2103 2104 #> [2,] 2105 2106 2107 2108 #> [3,] 2109 2110 2111 2112 #> [4,] 2113 2114 2115 2116 #> [5,] 2117 2118 2119 2120 #> #> $Loc_C$rep3 #> [,1] [,2] [,3] [,4] #> [1,] 2201 2202 2203 2204 #> [2,] 2205 2206 2207 2208 #> [3,] 2209 2210 2211 2212 #> [4,] 2213 2214 2215 2216 #> [5,] 2217 2218 2219 2220 #> #> $Loc_C$rep4 #> [,1] [,2] [,3] [,4] #> [1,] 2301 2302 2303 2304 #> [2,] 2305 2306 2307 2308 #> [3,] 2309 2310 2311 2312 #> [4,] 2313 2314 2315 2316 #> [5,] 2317 2318 2319 2320 #> #> $Loc_C$rep5 #> [,1] [,2] [,3] [,4] #> [1,] 2401 2402 2403 2404 #> [2,] 2405 2406 2407 2408 #> [3,] 2409 2410 2411 2412 #> [4,] 2413 2414 2415 2416 #> [5,] 2417 2418 2419 2420 #> #> $Loc_C$rep6 #> [,1] [,2] [,3] [,4] #> [1,] 2501 2502 2503 2504 #> [2,] 2505 2506 2507 2508 #> [3,] 2509 2510 2511 2512 #> [4,] 2513 2514 2515 2516 #> [5,] 2517 2518 2519 2520 #> #> head(strip2$fieldBook,12) #> ID LOCATION PLOT REP HSTRIP VSTRIP TRT_COMB #> 1 1 A 101 1 E D E|D #> 2 2 A 102 1 E B E|B #> 3 3 A 103 1 E C E|C #> 4 4 A 104 1 E A E|A #> 5 5 A 105 1 B D B|D #> 6 6 A 106 1 B B B|B #> 7 7 A 107 1 B C B|C #> 8 8 A 108 1 B A B|A #> 9 9 A 109 1 C D C|D #> 10 10 A 110 1 C B C|B #> 11 11 A 111 1 C C C|C #> 12 12 A 112 1 C A C|A"},{"path":"https://didiermurillof.github.io/FielDHub/reference/summary.FielDHub.html","id":null,"dir":"Reference","previous_headings":"","what":"Summary a FielDHub object β€” summary.FielDHub","title":"Summary a FielDHub object β€” summary.FielDHub","text":"Summarise information design parameters, data frame structure","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/summary.FielDHub.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summary a FielDHub object β€” summary.FielDHub","text":"","code":"# S3 method for class 'FielDHub' summary(object, ...)"},{"path":"https://didiermurillof.github.io/FielDHub/reference/summary.FielDHub.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summary a FielDHub object β€” summary.FielDHub","text":"object object inheriting class FielDHub ... Unused, extensibility","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/summary.FielDHub.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summary a FielDHub object β€” summary.FielDHub","text":"object inheriting class summary.FielDHub","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/summary.FielDHub.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Summary a FielDHub object β€” summary.FielDHub","text":"Thiago de Paula Oliveira, thiago.paula.oliveira@alumni.usp.br","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/summary.FielDHub.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summary a FielDHub object β€” summary.FielDHub","text":"","code":"# Example 1: Generates a CRD design with 5 treatments and 5 reps each. crd1 <- CRD(t = 5, reps = 5, plotNumber = 101, seed = 1985, locationName = \"Fargo\") crd1$infoDesign #> $numberofTreatments #> [1] 5 #> #> $treatments #> [1] \"T1\" \"T2\" \"T3\" \"T4\" \"T5\" #> #> $Reps #> [1] 5 #> #> $locationName #> [1] \"Fargo\" #> #> $seed #> [1] 1985 #> #> $id_design #> [1] 1 #> summary(crd1) #> Completely Randomized Design (CRD): #> #> 1. Information on the design parameters: #> List of 6 #> $ numberofTreatments: num 5 #> $ treatments : chr [1:5] \"T1\" \"T2\" \"T3\" \"T4\" ... #> $ Reps : num 5 #> $ locationName : chr \"Fargo\" #> $ seed : num 1985 #> $ id_design : num 1 #> #> 2. Structure of the data frame with the CRD field book: #> #> 'data.frame':\t25 obs. of 5 variables: #> $ ID : int 1 2 3 4 5 6 7 8 9 10 ... #> $ LOCATION : chr \"Fargo\" \"Fargo\" \"Fargo\" \"Fargo\" ... #> $ PLOT : int 101 102 103 104 105 106 107 108 109 110 ... #> $ REP : Factor w/ 5 levels \"1\",\"2\",\"3\",\"4\",..: 3 4 2 3 2 2 4 5 1 5 ... #> $ TREATMENT: chr \"T3\" \"T2\" \"T1\" \"T5\" ..."},{"path":"https://didiermurillof.github.io/FielDHub/reference/swap_pairs.html","id":null,"dir":"Reference","previous_headings":"","what":"Swap pairs in a matrix of integers β€” swap_pairs","title":"Swap pairs in a matrix of integers β€” swap_pairs","text":"Modifies input matrix X ensure distance two occurrences integer least dist d, swapping one occurrences random occurrence different integer least d away. function starts starting_dist = 3 increases 1 algorithm longer converges stop_iter iterations performed.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/swap_pairs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Swap pairs in a matrix of integers β€” swap_pairs","text":"","code":"swap_pairs(X, starting_dist = 3, stop_iter = 50)"},{"path":"https://didiermurillof.github.io/FielDHub/reference/swap_pairs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Swap pairs in a matrix of integers β€” swap_pairs","text":"X matrix integers. starting_dist minimum starting distance enforce pairs occurrences integer. Default 3. stop_iter maximum number iterations perform. Default 100.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/swap_pairs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Swap pairs in a matrix of integers β€” swap_pairs","text":"list containing following elements: optim_design modified matrix. designs list intermediate designs, starting input matrix. distances list pair distances intermediate design. min_distance integer indicating minimum distance pairs occurrences integer. pairwise_distance data frame pairwise distances final design.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/swap_pairs.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Swap pairs in a matrix of integers β€” swap_pairs","text":"Jean-Marc Montpetit [aut], Didier Murillo [aut]","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/swap_pairs.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Swap pairs in a matrix of integers β€” swap_pairs","text":"","code":"# Create a matrix X with the numbers 1 to 10 are twice and 11 to 50 are once. # The matrix has 6 rows and 10 columns set.seed(123) X <- matrix(sample(c(rep(1:10, 2), 11:50), replace = FALSE), ncol = 10) X #> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] #> [1,] 21 40 17 25 26 3 11 31 36 6 #> [2,] 5 33 5 8 48 29 43 23 1 45 #> [3,] 41 27 38 39 7 28 14 22 24 4 #> [4,] 4 47 18 7 2 35 6 20 12 46 #> [5,] 3 15 9 34 49 50 2 10 42 8 #> [6,] 32 16 19 9 10 13 37 1 44 30 # Swap pairs B <- swap_pairs(X, starting_dist = 3) B$optim_design #> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] #> [1,] 4 40 17 8 47 5 11 18 10 7 #> [2,] 7 30 22 3 48 29 34 44 25 1 #> [3,] 32 33 6 12 13 28 37 6 21 9 #> [4,] 9 1 20 2 19 35 43 38 15 16 #> [5,] 46 5 26 27 49 23 14 45 39 31 #> [6,] 36 8 42 41 50 10 24 3 4 2 B$designs #> [[1]] #> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] #> [1,] 21 40 17 25 26 3 11 31 36 6 #> [2,] 5 33 5 8 48 29 43 23 1 45 #> [3,] 41 27 38 39 7 28 14 22 24 4 #> [4,] 4 47 18 7 2 35 6 20 12 46 #> [5,] 3 15 9 34 49 50 2 10 42 8 #> [6,] 32 16 19 9 10 13 37 1 44 30 #> #> [[2]] #> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] #> [1,] 21 40 17 25 26 3 11 9 23 6 #> [2,] 7 33 15 36 48 29 43 8 1 45 #> [3,] 41 8 38 39 13 28 14 22 24 2 #> [4,] 4 47 18 2 9 35 6 20 12 46 #> [5,] 3 5 31 4 49 50 34 10 42 7 #> [6,] 32 16 19 27 10 5 37 1 44 30 #> #> [[3]] #> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] #> [1,] 21 40 17 25 26 3 11 9 23 6 #> [2,] 7 33 15 36 48 29 43 8 4 45 #> [3,] 41 8 38 39 13 28 14 22 24 2 #> [4,] 46 47 18 2 9 35 6 20 12 10 #> [5,] 10 5 31 1 49 50 34 3 42 7 #> [6,] 32 16 19 27 4 5 37 1 44 30 #> #> [[4]] #> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] #> [1,] 4 40 17 8 47 5 11 18 10 7 #> [2,] 7 30 22 3 48 29 34 44 25 1 #> [3,] 32 33 6 12 13 28 37 6 21 9 #> [4,] 9 1 20 2 19 35 43 38 15 16 #> [5,] 46 5 26 27 49 23 14 45 39 31 #> [6,] 36 8 42 41 50 10 24 3 4 2 #> B$distances #> [[1]] #> geno Pos1 Pos2 DIST rA cA rB cB #> 7 7 22 27 1.414214 4 4 3 5 #> 9 9 17 24 1.414214 5 3 6 4 #> 5 5 2 14 2.000000 2 1 2 3 #> 2 2 28 41 2.236068 4 5 5 7 #> 10 10 30 47 3.162278 6 5 5 8 #> 1 1 48 50 4.123106 6 8 2 9 #> 6 6 40 55 4.242641 4 7 1 10 #> 3 3 5 31 6.403124 5 1 1 6 #> 8 8 20 59 6.708204 2 4 5 10 #> 4 4 4 57 9.055385 4 1 3 10 #> #> [[2]] #> geno Pos1 Pos2 DIST rA cA rB cB #> 4 4 4 23 3.162278 4 1 5 4 #> 10 10 30 47 3.162278 6 5 5 8 #> 1 1 48 50 4.123106 6 8 2 9 #> 5 5 11 36 4.123106 5 2 6 6 #> 6 6 40 55 4.242641 4 7 1 10 #> 9 9 28 43 4.242641 4 5 1 8 #> 2 2 22 57 6.082763 4 4 3 10 #> 8 8 9 44 6.082763 3 2 2 8 #> 3 3 5 31 6.403124 5 1 1 6 #> 7 7 2 59 9.486833 2 1 5 10 #> #> [[3]] #> geno Pos1 Pos2 DIST rA cA rB cB #> 1 1 23 48 4.123106 5 4 6 8 #> 5 5 11 36 4.123106 5 2 6 6 #> 6 6 40 55 4.242641 4 7 1 10 #> 9 9 28 43 4.242641 4 5 1 8 #> 3 3 31 47 4.472136 1 6 5 8 #> 4 4 30 50 5.656854 6 5 2 9 #> 2 2 22 57 6.082763 4 4 3 10 #> 8 8 9 44 6.082763 3 2 2 8 #> 10 10 5 58 9.055385 5 1 4 10 #> 7 7 2 59 9.486833 2 1 5 10 #> #> [[4]] #> geno Pos1 Pos2 DIST rA cA rB cB #> 6 6 15 45 5.000000 3 3 3 8 #> 8 8 12 19 5.385165 6 2 1 4 #> 3 3 20 48 5.656854 2 4 6 8 #> 5 5 11 31 5.656854 5 2 1 6 #> 10 10 36 49 5.830952 6 6 1 9 #> 2 2 22 60 6.324555 4 4 6 10 #> 1 1 10 56 8.246211 4 2 2 10 #> 7 7 2 55 9.055385 2 1 1 10 #> 9 9 4 57 9.055385 4 1 3 10 #> 4 4 1 54 9.433981 1 1 6 9 #>"},{"path":"https://didiermurillof.github.io/FielDHub/news/index.html","id":"fieldhub-131","dir":"Changelog","previous_headings":"","what":"FielDHub 1.3.1","title":"FielDHub 1.3.1","text":"CRAN release: 2023-04-20","code":""},{"path":"https://didiermurillof.github.io/FielDHub/news/index.html","id":"new-features-in-the-shiny-app-1-3-1","dir":"Changelog","previous_headings":"","what":"New Features in the Shiny App","title":"FielDHub 1.3.1","text":"Added module generate Sparse allocation. Added module generating Optimized Multi-Location Partially Replicated (p-rep). Added vignettes help documentation new modules; Sparse Allocations Optimized Multi-Location Partially Replicated (p-rep) Designs app.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/news/index.html","id":"enhancements-1-3-1","dir":"Changelog","previous_headings":"","what":"Enhancements:","title":"FielDHub 1.3.1","text":"Renamed Partially Replicated module Single Multi-Location p-rep Improved usability field dimensions dropdown menu reordering options based absolute value difference number rows columns option. affects unreplicated partially replicated design modules.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/news/index.html","id":"fix-bugs-1-3-1","dir":"Changelog","previous_headings":"","what":"Fix bugs:","title":"FielDHub 1.3.1","text":"Fixed issue: Upload data CRD module.","code":""},{"path":[]},{"path":"https://didiermurillof.github.io/FielDHub/news/index.html","id":"standalone-functions-1-3-1","dir":"Changelog","previous_headings":"","what":"Standalone Functions","title":"FielDHub 1.3.1","text":"Created do_optim() function. function generates sparse p-rep allocation multiple locations. optimized allocation using incomplete blocks. Created sparse_allocation() function. new function uses function, do_optim(), generate sparse allocation, uses function diagonal_arrangement() create unreplicated designs across multiple locations. Created multi_location_prep() function. uses within optimization function do_optim() generate partially replicated (p-rep) allocation, uses function partially_replicated() create p-rep designs across multiple locations. Created pairs_distance() function. function calculates pairwise distances elements matrix appears twice . Created swap_pairs() function. swaps pairs matrix integers optimizes p-rep design. function modifies input matrix XX ensure distance two occurrences integer least distance dd, swapping one occurrences random occurrence different integer least dd away. function starts starting dist d=3d = 3 increases 11 algorithm longer converges max number iterations performed. Created search_matrix_values() function. looks values appear row matrix return row number, value, frequency. Added optimization process partially replicated (p-rep) designs. uses function swap_pairs(). Added vignettes help documentation new functions.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/news/index.html","id":"enhancements-1-3-1-1","dir":"Changelog","previous_headings":"","what":"Enhancements:","title":"FielDHub 1.3.1","text":"partially_replicated() accepts custom field dimensions location. example, nrows = c(23, 20, 20) ncols = c(20, 23, 23) field rows columns three environments. Code refactoring diagonal_arrangement() function. Code refactoring utility function pREP(). Avoid cyclic reps incomplete block designs number treatments square.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/news/index.html","id":"fieldhub-120","dir":"Changelog","previous_headings":"","what":"FielDHub 1.2.0","title":"FielDHub 1.2.0","text":"CRAN release: 2022-08-05","code":""},{"path":"https://didiermurillof.github.io/FielDHub/news/index.html","id":"shiny-app-1-2-0","dir":"Changelog","previous_headings":"","what":"Shiny App","title":"FielDHub 1.2.0","text":"Added help menu option app connect directly documentation available GitHub repository. Added vignettes help documentation standard functions modules available designs app. Added capability making multiple randomizations across different locations unreplicated, partially replicated, lattice, RCBD, factorial, split-plot, split-split-plot, strip-plot, IBD, RCD designs. Added capability produce heatmap visualizations simulated data experimental designs. Added action buttons copy save field maps field book outputs Excel. Added factorization options aid users creation randomizations mapping layouts unreplicated partially replicated designs. Previous version required users * mathematical calculation priori. Added filters search boxes field book tables. Updated UI/UX design home page. Grouped single diagonal arrangement, multiple diagonal arrangement, optimized arrangement augmented RCB designs one single module. Added action run button experimental designs prevent reactivity issues application. Improved standardized user experience features readability access. Improved error logging messages. Added features inform end-users utilization correct input data file formats associated metadata/columns, checking duplicate values input files, well data type verification. Added plot() method FielDHub package display field layout field book designs. Added additional field layout visualization/map options experimental designs. Previous version mapping options unreplicated p-rep designs. Added drop-menu display multiple layout mapping option shown entry number plot experimental designs. means, now can visualize randomization layout option locations input. Added option repeating whole entries/experiments unreplicated diagonal arrangement design multiple experiments (previously called decision blocks). Added check box feature Augmented RCB design allow creation nurseries option randomizing experimental entries . user decides leave option unchecked, checks randomized, experimental entries shown consecutive order. Added check box option RCB design allow continuous plot numbering independently rep block number. Previous version coded replication plot number (.e., 101 =rep1, 201=rep2, etc.). Fixed restriction RCBD mapping layout allow use 25 entries. PS: better designs number entries higher 25 (info go : FIELD PLOT DESIGN ).","code":""},{"path":"https://didiermurillof.github.io/FielDHub/news/index.html","id":"standalone-functions-in-fieldhub-package-1-2-0","dir":"Changelog","previous_headings":"","what":"Standalone Functions in FielDHub Package","title":"FielDHub 1.2.0","text":"partially_replicated() now generates randomization across multiple locations/sites. diagonal_arrangement() now generates randomization across multiple locations/sites. optimized_arrangement() now generates randomization across multiple locations/sites. partially_replicated() now allows entries/treatments replicates. , required least unreplicated entries. Functions optimized_arrangement(), diagonal_arrangement() partially_replicated() now return feedback input dimensions nrows ncols incorrect. RCBD() now includes argument (continuous) manage way sets plotting number. RCBD_augmented() now allows customization field dimensions inputting number rows columns nrows ncols arguments. RCBD_augmented() now returns feedback input dimensions nrows ncols match data entered. RCBD_augmented() random = FALSE now allows randomizing checks/controls user wants. Fixed bug full_factorial() CRD factorial design prevented option including possible factorial combinations. Added method print() class fieldLayout. See print(). Added method plot() class FieldHub returns object class fieldLayout. See plot(). method plot() can plot field layout designs output. possible pass arguments location, layout order others. detail see plot(), print() summary() methods FielDHub. Fixed bug diagonal_arrangement() kindExpt = DBUDC. problem affected random distribution checks case unbalanced control plot numbers experiment. Fixed bug diagonal_arrangement() kindExpt = DBUDC. problem affected merging data user data randomization data users wanted replicated entries across experiments.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/news/index.html","id":"fieldhub-010","dir":"Changelog","previous_headings":"","what":"FielDHub 0.1.0","title":"FielDHub 0.1.0","text":"CRAN release: 2021-05-19 CRAN release.","code":""}] +[{"path":[]},{"path":"https://didiermurillof.github.io/FielDHub/CODE_OF_CONDUCT.html","id":"our-pledge","dir":"","previous_headings":"","what":"Our Pledge","title":"Contributor Covenant Code of Conduct","text":"members, contributors, leaders pledge make participation community harassment-free experience everyone, regardless age, body size, visible invisible disability, ethnicity, sex characteristics, gender identity expression, level experience, education, socio-economic status, nationality, personal appearance, race, religion, sexual identity orientation. pledge act interact ways contribute open, welcoming, diverse, inclusive, healthy community.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/CODE_OF_CONDUCT.html","id":"our-standards","dir":"","previous_headings":"","what":"Our Standards","title":"Contributor Covenant Code of Conduct","text":"Examples behavior contributes positive environment community include: Demonstrating empathy kindness toward people respectful differing opinions, viewpoints, experiences Giving gracefully accepting constructive feedback Accepting responsibility apologizing affected mistakes, learning experience Focusing best just us individuals, overall community Examples unacceptable behavior include: use sexualized language imagery, sexual attention advances kind Trolling, insulting derogatory comments, personal political attacks Public private harassment Publishing others’ private information, physical email address, without explicit permission conduct reasonably considered inappropriate professional setting","code":""},{"path":"https://didiermurillof.github.io/FielDHub/CODE_OF_CONDUCT.html","id":"enforcement-responsibilities","dir":"","previous_headings":"","what":"Enforcement Responsibilities","title":"Contributor Covenant Code of Conduct","text":"Community leaders responsible clarifying enforcing standards acceptable behavior take appropriate fair corrective action response behavior deem inappropriate, threatening, offensive, harmful. Community leaders right responsibility remove, edit, reject comments, commits, code, wiki edits, issues, contributions aligned Code Conduct, communicate reasons moderation decisions appropriate.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/CODE_OF_CONDUCT.html","id":"scope","dir":"","previous_headings":"","what":"Scope","title":"Contributor Covenant Code of Conduct","text":"Code Conduct applies within community spaces, also applies individual officially representing community public spaces. Examples representing community include using official e-mail address, posting via official social media account, acting appointed representative online offline event.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/CODE_OF_CONDUCT.html","id":"enforcement","dir":"","previous_headings":"","what":"Enforcement","title":"Contributor Covenant Code of Conduct","text":"Instances abusive, harassing, otherwise unacceptable behavior may reported community leaders responsible enforcement [email][didier.murilloflorez@gmail.com]. complaints reviewed investigated promptly fairly. community leaders obligated respect privacy security reporter incident.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/CODE_OF_CONDUCT.html","id":"enforcement-guidelines","dir":"","previous_headings":"","what":"Enforcement Guidelines","title":"Contributor Covenant Code of Conduct","text":"Community leaders follow Community Impact Guidelines determining consequences action deem violation Code Conduct:","code":""},{"path":"https://didiermurillof.github.io/FielDHub/CODE_OF_CONDUCT.html","id":"id_1-correction","dir":"","previous_headings":"Enforcement Guidelines","what":"1. Correction","title":"Contributor Covenant Code of Conduct","text":"Community Impact: Use inappropriate language behavior deemed unprofessional unwelcome community. Consequence: private, written warning community leaders, providing clarity around nature violation explanation behavior inappropriate. public apology may requested.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/CODE_OF_CONDUCT.html","id":"id_2-warning","dir":"","previous_headings":"Enforcement Guidelines","what":"2. Warning","title":"Contributor Covenant Code of Conduct","text":"Community Impact: violation single incident series actions. Consequence: warning consequences continued behavior. interaction people involved, including unsolicited interaction enforcing Code Conduct, specified period time. includes avoiding interactions community spaces well external channels like social media. Violating terms may lead temporary permanent ban.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/CODE_OF_CONDUCT.html","id":"id_3-temporary-ban","dir":"","previous_headings":"Enforcement Guidelines","what":"3. Temporary Ban","title":"Contributor Covenant Code of Conduct","text":"Community Impact: serious violation community standards, including sustained inappropriate behavior. Consequence: temporary ban sort interaction public communication community specified period time. public private interaction people involved, including unsolicited interaction enforcing Code Conduct, allowed period. Violating terms may lead permanent ban.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/CODE_OF_CONDUCT.html","id":"id_4-permanent-ban","dir":"","previous_headings":"Enforcement Guidelines","what":"4. Permanent Ban","title":"Contributor Covenant Code of Conduct","text":"Community Impact: Demonstrating pattern violation community standards, including sustained inappropriate behavior, harassment individual, aggression toward disparagement classes individuals. Consequence: permanent ban sort public interaction within community.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/CODE_OF_CONDUCT.html","id":"attribution","dir":"","previous_headings":"","what":"Attribution","title":"Contributor Covenant Code of Conduct","text":"Code Conduct adapted Contributor Covenant, version 2.0, available https://www.contributor-covenant.org/version/2/0/code_of_conduct.html. Community Impact Guidelines inspired Mozilla’s code conduct enforcement ladder. answers common questions code conduct, see FAQ https://www.contributor-covenant.org/faq. Translations available https://www.contributor-covenant.org/translations.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/CONTRIBUTING.html","id":null,"dir":"","previous_headings":"","what":"Contributing to FielDHub","title":"Contributing to FielDHub","text":"First , thanks considering contributing FielDHub! πŸ‘ ’s people like make rewarding us - project maintainers - work FielDHub. 😊 FielDHub open-source project, maintained people care.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/CONTRIBUTING.html","id":"code-of-conduct","dir":"","previous_headings":"","what":"Code of conduct","title":"Contributing to FielDHub","text":"Please note project released Contributor Code Conduct. participating project agree abide terms.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/CONTRIBUTING.html","id":"how-you-can-contribute","dir":"","previous_headings":"","what":"How you can contribute","title":"Contributing to FielDHub","text":"several ways can contribute project. want know contribute open source projects like one, see Open Source Guide.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/CONTRIBUTING.html","id":"share-the-love-️","dir":"","previous_headings":"How you can contribute","what":"Share the love ❀️","title":"Contributing to FielDHub","text":"Think FielDHub useful? Let others discover , telling person, via Twitter blog post. Using FielDHub paper writing? Consider citing .","code":""},{"path":"https://didiermurillof.github.io/FielDHub/CONTRIBUTING.html","id":"ask-a-question-️","dir":"","previous_headings":"How you can contribute","what":"Ask a question ⁉️","title":"Contributing to FielDHub","text":"Using FielDHub got stuck? Browse documentation see can find solution. Still stuck? Post question issue GitHub. offer user support, ’ll try best address , questions often lead better documentation discovery bugs. Want ask question private? Contact package maintainer [email][didier.murilloflorez@ndsu.edu].","code":""},{"path":"https://didiermurillof.github.io/FielDHub/CONTRIBUTING.html","id":"propose-an-idea-","dir":"","previous_headings":"How you can contribute","what":"Propose an idea πŸ’‘","title":"Contributing to FielDHub","text":"idea new FielDHub feature? Take look documentation issue list see isn’t included suggested yet. , suggest idea issue GitHub. can’t promise implement idea, helps : Explain detail work. Keep scope narrow possible. See want contribute code idea well.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/CONTRIBUTING.html","id":"report-a-bug-","dir":"","previous_headings":"How you can contribute","what":"Report a bug πŸ›","title":"Contributing to FielDHub","text":"Using FielDHub discovered bug? ’s annoying! Don’t let others experience report issue GitHub can fix . good bug report makes easier us , please include: operating system name version (e.g.Β Mac OS 10.13.6). details local setup might helpful troubleshooting. Detailed steps reproduce bug.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/CONTRIBUTING.html","id":"improve-the-documentation-","dir":"","previous_headings":"How you can contribute","what":"Improve the documentation πŸ“–","title":"Contributing to FielDHub","text":"Noticed typo website? Think function use better example? Good documentation makes difference, help improve welcome!","code":""},{"path":"https://didiermurillof.github.io/FielDHub/CONTRIBUTING.html","id":"the-website","dir":"","previous_headings":"How you can contribute > Improve the documentation πŸ“–","what":"The website","title":"Contributing to FielDHub","text":"website generated pkgdown. means don’t write html: content pulled together documentation code, vignettes, Markdown files, package DESCRIPTION _pkgdown.yml settings. know way around pkgdown, can propose file change improve documentation. , report issue can point right direction.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/CONTRIBUTING.html","id":"function-documentation","dir":"","previous_headings":"How you can contribute > Improve the documentation πŸ“–","what":"Function documentation","title":"Contributing to FielDHub","text":"Functions described comments near code translated documentation using roxygen2. want improve function description: Go R/ directory code repository. Look file name function. Propose file change update function documentation roxygen comments (starting #').","code":""},{"path":"https://didiermurillof.github.io/FielDHub/CONTRIBUTING.html","id":"contribute-code-","dir":"","previous_headings":"How you can contribute","what":"Contribute code πŸ“","title":"Contributing to FielDHub","text":"Care fix bugs implement new functionality FielDHub? Awesome! πŸ‘ look issue list leave comment things want work . See also development guidelines .","code":""},{"path":"https://didiermurillof.github.io/FielDHub/CONTRIBUTING.html","id":"development-guidelines","dir":"","previous_headings":"","what":"Development guidelines","title":"Contributing to FielDHub","text":"try follow GitHub flow development. Fork repo clone computer. learn process, see guide. forked cloned project since worked , pull changes original repo clone using git pull upstream master. Open RStudio project file (.Rproj). Write code. Test code (bonus points adding unit tests). Document code (see function documentation ). Check code devtools::check() aim 0 errors, warnings notes. Commit push changes. Submit pull request.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/CONTRIBUTING.html","id":"attribution","dir":"","previous_headings":"","what":"Attribution","title":"Contributing to FielDHub","text":"Contributing adapted CONTRIBUTING.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"MIT License","title":"MIT License","text":"Copyright (c) 2021 North Dakota State University (NDSU) Permission hereby granted, free charge, person obtaining copy software associated documentation files (β€œSoftware”), deal Software without restriction, including without limitation rights use, copy, modify, merge, publish, distribute, sublicense, /sell copies Software, permit persons Software furnished , subject following conditions: copyright notice permission notice shall included copies substantial portions Software. SOFTWARE PROVIDED β€œβ€, WITHOUT WARRANTY KIND, EXPRESS IMPLIED, INCLUDING LIMITED WARRANTIES MERCHANTABILITY, FITNESS PARTICULAR PURPOSE NONINFRINGEMENT. EVENT SHALL AUTHORS COPYRIGHT HOLDERS LIABLE CLAIM, DAMAGES LIABILITY, WHETHER ACTION CONTRACT, TORT OTHERWISE, ARISING , CONNECTION SOFTWARE USE DEALINGS SOFTWARE.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/RCBD_augmented.html","id":"overview","dir":"Articles","previous_headings":"","what":"Overview","title":"Augmented Randomized Complete Block Design","text":"augmented randomized complete block design another option overcome problem limited facilities lack seed researchers want test many treatments. kind design, approach build augmented blocks allocate amount controls every block along treatments. FielDHub includes function run experimental designs, features include options set number entries number checks augmented blocks experiment. Users can also choose run experiment multiple locations.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/RCBD_augmented.html","id":"use-case","dir":"Articles","previous_headings":"","what":"Use case","title":"Augmented Randomized Complete Block Design","text":"example, say project needs test 120 genotypes cassava two locations. addition, research includes four checks six augmented blocks carry experiment. design setup comes 6 blocks size 24 plots total 144 plots distributed field 12 rows 12 columns.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/RCBD_augmented.html","id":"running-the-shiny-app","dir":"Articles","previous_headings":"","what":"Running the Shiny App","title":"Augmented Randomized Complete Block Design","text":"launch app need run either ","code":"FielDHub::run_app() library(FielDHub) run_app()"},{"path":"https://didiermurillof.github.io/FielDHub/articles/RCBD_augmented.html","id":"using-the-fieldhub-shiny-app","dir":"Articles","previous_headings":"","what":"1. Using the FielDHub Shiny App","title":"Augmented Randomized Complete Block Design","text":"app running, go Unreplicated Designs > RCBD Augmented , follow following steps show generate RCBD Augmented.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/RCBD_augmented.html","id":"inputs","dir":"Articles","previous_headings":"","what":"Inputs","title":"Augmented Randomized Complete Block Design","text":"selection , means app going generate synthetic data entries names treatment/genotypes based user inputs. selection Yes, entries list must fulfill specific format must .csv file. file must columns ENTRY NAME. ENTRY column must unique entry integer number treatment/genotype. column NAME must unique name identifies treatment/genotype. ENTRY NAME must unique, duplicates allowed. following table, show example entries list format. example entry list four checks 8 treatments/genotypes. crucial allocate checks top part file. Enter number stacked experiments Input # Stacked Expts box. means number times experiment replicated. case perform just 1 experiment. augmented RCBD option choose whether randomize entries , Randomize Entries toggle button. recommended always randomized treatments/entries researchers choose randomize treatments, often due logistical issues. Enter number entries/treatments Input # Entries box, 120 example experiment. Set number checks per block Checks per Block box. case, 5 checks. Set number blocks Input # Blocks box, 6 example. total number plots field Input # Stacked Expts(Input # Entries + Input # Blocks * Checks per Block), per location. Enter number locations Input # Locations. Set 2. Select serpentine cartesian Plot Order Layout. example set serpentine layout. ensure randomizations consistent across sessions, can set random seed box labeled random seed. example, set 1987. Enter starting plot number Starting Plot Number box. experiment multiple locations, must enter comma separated list numbers length number locations input valid. entered information experiment left side panel, click Run! button run design. prompted select dimensions field list options drop middle screen box labeled Select dimensions field. case, select 12 x 12. Click Randomize! button randomize experiment set field dimensions see output plots. change dimensions , must re-randomize. change inputs left side panel running experiment initially, click Run Randomize buttons , re-run new inputs.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/RCBD_augmented.html","id":"outputs","dir":"Articles","previous_headings":"","what":"Outputs","title":"Augmented Randomized Complete Block Design","text":"run augmented RCBD FielDHub set dimensions field, several ways display information contained field book. first tab, Get Random, shows option change dimensions field re-randomize, well reference guide experiment design.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/RCBD_augmented.html","id":"input-data","dir":"Articles","previous_headings":"Outputs","what":"Input Data","title":"Augmented Randomized Complete Block Design","text":"second tab, Input Data, can see entries randomization list, well table checks number times appear field. list entries, reps check included well.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/RCBD_augmented.html","id":"randomized-field","dir":"Articles","previous_headings":"Outputs","what":"Randomized Field","title":"Augmented Randomized Complete Block Design","text":"Randomized Field tab displays graphical representation randomization entries field specified dimensions. checks colored uniquely, showing number times distributed throughout field. display includes numbered labels rows columns. can copy field table save directly Excel file Copy Excel buttons top.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/RCBD_augmented.html","id":"plot-number-field","dir":"Articles","previous_headings":"Outputs","what":"Plot Number Field","title":"Augmented Randomized Complete Block Design","text":"Plot Number Field tab, table display field plots numbered according Plot Order Layout specified, either serpentine cartesian. can see corresponding entries plot number field book. Like Randomized Field tab, can copy table save Excel file Copy Excel buttons.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/RCBD_augmented.html","id":"field-book","dir":"Articles","previous_headings":"Outputs","what":"Field Book","title":"Augmented Randomized Complete Block Design","text":"Field Book displays information experimental design table format. contains specific plot number row column address entry, well corresponding treatment plot. table searchable, can filter data relevant columns.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/RCBD_augmented.html","id":"using-the-fieldhub-function-rcbd_augmented","dir":"Articles","previous_headings":"","what":"2. Using the FielDHub function: RCBD_augmented()","title":"Augmented Randomized Complete Block Design","text":"can run design function RCBD_augmented() FielDHub package. First, need load FielDHub package typing, , can enter information describing design like :","code":"library(FielDHub) aug_RCBD <- RCBD_augmented( lines = 120, checks = 4, b = 6, repsExpt = 1, l = 2, random = TRUE, exptName = \"Cassava_2022\", plotNumber = c(1001, 2001), locationNames = c(\"FARGO\", \"CASSELTON\"), nrows = 12, ncols = 12, seed = 1987 )"},{"path":"https://didiermurillof.github.io/FielDHub/articles/RCBD_augmented.html","id":"details-on-the-inputs-entered-in-rcbd_augmented-above","dir":"Articles","previous_headings":"2. Using the FielDHub function: RCBD_augmented()","what":"Details on the inputs entered in RCBD_augmented() above","title":"Augmented Randomized Complete Block Design","text":"description inputs used generate design, lines = 120 number entries checks = 4 number checks augmented block. b = 6 number augmented blocks. repsExpt = 1 number reps experiment. l = 2 number locations. random = TRUE means treatment/entries checks randomized. exptName = \"Cassava_2022\" optional name experiment. plotNumber = c(1001,2001) starting plot number location respectively, single number 1 location. locationNames = c(\"FARGO\", \"CASSELTON\") values representing respective name location. nrows = 12 number rows field. optional ncols = 12 number columns field. optional. seed = 1987 random seed replicate identical randomizations.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/RCBD_augmented.html","id":"print-aug_rcbd-output","dir":"Articles","previous_headings":"2. Using the FielDHub function: RCBD_augmented()","what":"Print aug_RCBD output","title":"Augmented Randomized Complete Block Design","text":"print summary information object aug_RCBD, can use generic function print().","code":"print(aug_RCBD) Augmented Randomized Complete Block Design: Information on the design parameters: List of 11 $ rows : num 12 $ columns : num 12 $ rows_within_blocks : num 2 $ columns_within_blocks: num 12 $ treatments : num 120 $ checks : num 4 $ blocks : num 6 $ plots_per_block : num [1:6] 24 24 24 24 24 24 $ locations : num 2 $ fillers : num 0 $ seed : num 1987 10 First observations of the data frame with the RCBD_augmented field book: ID EXPT LOCATION YEAR PLOT ROW COLUMN CHECKS BLOCK ENTRY TREATMENT 1 1 Cassava_2022 FARGO 2024 1001 1 1 0 1 98 G98 2 2 Cassava_2022 FARGO 2024 1002 1 2 0 1 103 G103 3 3 Cassava_2022 FARGO 2024 1003 1 3 0 1 87 G87 4 4 Cassava_2022 FARGO 2024 1004 1 4 1 1 2 CH2 5 5 Cassava_2022 FARGO 2024 1005 1 5 0 1 21 G21 6 6 Cassava_2022 FARGO 2024 1006 1 6 0 1 122 G122 7 7 Cassava_2022 FARGO 2024 1007 1 7 1 1 4 CH4 8 8 Cassava_2022 FARGO 2024 1008 1 8 0 1 44 G44 9 9 Cassava_2022 FARGO 2024 1009 1 9 0 1 23 G23 10 10 Cassava_2022 FARGO 2024 1010 1 10 0 1 113 G113"},{"path":"https://didiermurillof.github.io/FielDHub/articles/RCBD_augmented.html","id":"access-to-aug_rcbd-output","dir":"Articles","previous_headings":"2. Using the FielDHub function: RCBD_augmented()","what":"Access to aug_RCBD output","title":"Augmented Randomized Complete Block Design","text":"function RCBD_augmented() returns list consisting information displayed output tabs FielDHub app: design information, plot layout, plot numbering, entries list, field book. accessible $ operator, .e.Β aug_RCBD$layoutRandom aug_RCBD$fieldBook. aug_RCBD$fieldBook list containing information every plot field, information location plot treatment plot. seen output , field book columns ID, EXPT, LOCATION, YEAR, PLOT, ROW, COLUMN, CHECKS, ENTRY, TREATMENT. Let us see first 10 rows field book experiment.","code":"field_book <- aug_RCBD$fieldBook head(field_book, 10) ID EXPT LOCATION YEAR PLOT ROW COLUMN CHECKS BLOCK ENTRY TREATMENT 1 1 Cassava_2022 FARGO 2024 1001 1 1 0 1 98 G98 2 2 Cassava_2022 FARGO 2024 1002 1 2 0 1 103 G103 3 3 Cassava_2022 FARGO 2024 1003 1 3 0 1 87 G87 4 4 Cassava_2022 FARGO 2024 1004 1 4 1 1 2 CH2 5 5 Cassava_2022 FARGO 2024 1005 1 5 0 1 21 G21 6 6 Cassava_2022 FARGO 2024 1006 1 6 0 1 122 G122 7 7 Cassava_2022 FARGO 2024 1007 1 7 1 1 4 CH4 8 8 Cassava_2022 FARGO 2024 1008 1 8 0 1 44 G44 9 9 Cassava_2022 FARGO 2024 1009 1 9 0 1 23 G23 10 10 Cassava_2022 FARGO 2024 1010 1 10 0 1 113 G113"},{"path":"https://didiermurillof.github.io/FielDHub/articles/RCBD_augmented.html","id":"plot-field-layout","dir":"Articles","previous_headings":"2. Using the FielDHub function: RCBD_augmented()","what":"Plot field layout","title":"Augmented Randomized Complete Block Design","text":"plotting layout function coordinates ROW COLUMN field book object can use generic function plot() follow,","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/RCBD_augmented.html","id":"plot-layout-for-location-1","dir":"Articles","previous_headings":"2. Using the FielDHub function: RCBD_augmented() > Plot field layout","what":"Plot layout for location 1","title":"Augmented Randomized Complete Block Design","text":"possible pass arguments plot() specific location. example, can plot layout location 2.","code":"plot(aug_RCBD)"},{"path":"https://didiermurillof.github.io/FielDHub/articles/RCBD_augmented.html","id":"plot-layout-for-location-2","dir":"Articles","previous_headings":"2. Using the FielDHub function: RCBD_augmented() > Plot field layout","what":"Plot layout for location 2","title":"Augmented Randomized Complete Block Design","text":"","code":"plot(aug_RCBD, l = 2)"},{"path":"https://didiermurillof.github.io/FielDHub/articles/alpha_lattice.html","id":"using-the-fieldhub-shiny-app","dir":"Articles","previous_headings":"","what":"1. Using the FielDHub Shiny App","title":"Alpha Lattice Design","text":"launch app need run either app running, go Lattice Designs > Alpha Lattice , follow following steps show generate kind design 55 treatments 3 reps.","code":"FielDHub::run_app() library(FielDHub) run_app()"},{"path":"https://didiermurillof.github.io/FielDHub/articles/alpha_lattice.html","id":"inputs","dir":"Articles","previous_headings":"","what":"Inputs","title":"Alpha Lattice Design","text":"selection , means app going generate synthetic data entries names treatment/genotypes based user inputs. selection Yes, entries list must fulfill specific format must .csv file. file must columns ENTRY NAME. ENTRY column must unique entry integer number treatment. column NAME must unique name identifies treatment. ENTRY NAME must unique, duplicates allowed. following table, show example entries list format. example entry list 12 treatments. Input number treatments Input # Treatments box. alpha lattice design, number treatments must composite number. case, set 55. Select number replications treatments Input # Full Reps box. Set 3. Set number plots incomplete block Input # Plots per IBlock box. Set 5. Enter number locations Input # Locations. run experiment single location, set 1. Select serpentine cartesian Plot Order Layout. example use default cartesian layout. Enter starting plot number Starting Plot Number box. experiment multiple locations, must enter comma separated list numbers length number locations input valid. Set 101. Enter name location experiment Input Location box. multiple locations, name must comma separated list. example, set \"FARGO\". ensure randomizations consistent across sessions, can set random seed box labeled random seed. example, set 1235. entered information experiment left side panel, click Run! button run design.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/alpha_lattice.html","id":"outputs","dir":"Articles","previous_headings":"","what":"Outputs","title":"Alpha Lattice Design","text":"run alpha lattice design FielDHub, several ways display information contained field book.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/alpha_lattice.html","id":"field-layout","dir":"Articles","previous_headings":"Outputs","what":"Field Layout","title":"Alpha Lattice Design","text":"first click run button alpha lattice design, FielDHub displays Field Layout tab, shows entries arrangement field. box display, can change layout field change location displayed. can also display heatmap field changing Type Plot Heatmap. view heatmap, must first simulate experiment described field Simulate! button. pop-window appear can enter variable want simulate along minimum maximum values.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/alpha_lattice.html","id":"field-book","dir":"Articles","previous_headings":"Outputs","what":"Field Book","title":"Alpha Lattice Design","text":"Field Book displays information experimental design table format. contains specific plot number row column address entry, well corresponding treatment plot. table searchable, can filter data relevant columns. simulated data heatmap, additional column variable appears Field Book.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/alpha_lattice.html","id":"using-the-fieldhub-function-alpha_lattice","dir":"Articles","previous_headings":"","what":"2. Using the FielDHub function: alpha_lattice()","title":"Alpha Lattice Design","text":"can run design function FielDHub package, alpha_lattice(). First, need load FielDHub package typing, , can enter information describing design like :","code":"library(FielDHub) alpha <- alpha_lattice( t = 55, r = 3, k = 5, l = 1, plotNumber = 101, locationNames = \"FARGO\", seed = 1235 )"},{"path":"https://didiermurillof.github.io/FielDHub/articles/alpha_lattice.html","id":"details-on-the-inputs-entered-in-alpha_lattice-above","dir":"Articles","previous_headings":"2. Using the FielDHub function: alpha_lattice()","what":"Details on the inputs entered in alpha_lattice() above","title":"Alpha Lattice Design","text":"description inputs used generate design, t = 55 number treatments. r=3 number replicates. k = 5 number plots per incomplete block. l = 1 number locations. plotNumber = 101 starting plot number. locationNames = \"FARGO\" optional name location. seed = 1235 random seed replicate identical randomizations.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/alpha_lattice.html","id":"print-alpha-object","dir":"Articles","previous_headings":"2. Using the FielDHub function: alpha_lattice()","what":"Print alpha object","title":"Alpha Lattice Design","text":"print summary information object alpha, can use generic function print().","code":"print(alpha) Alpha Lattice Design Efficiency of design: Level Blocks D-Efficiency A-Efficiency A-Bound 1 1 3 1.0000000 1.0000000 1.0000000 2 2 33 0.7857467 0.7545589 0.7574115 Information on the design parameters: List of 7 $ Reps : num 3 $ iBlocks : num 11 $ NumberTreatments: num 55 $ NumberLocations : num 1 $ Locations : chr \"FARGO\" $ seed : num 1235 $ lambda : num 0.222 10 First observations of the data frame with the alpha_lattice field book: ID LOCATION PLOT REP IBLOCK UNIT ENTRY TREATMENT 1 1 FARGO 101 1 1 1 15 G-15 2 2 FARGO 102 1 1 2 8 G-8 3 3 FARGO 103 1 1 3 51 G-51 4 4 FARGO 104 1 1 4 54 G-54 5 5 FARGO 105 1 1 5 4 G-4 6 6 FARGO 106 1 2 1 50 G-50 7 7 FARGO 107 1 2 2 40 G-40 8 8 FARGO 108 1 2 3 42 G-42 9 9 FARGO 109 1 2 4 22 G-22 10 10 FARGO 110 1 2 5 16 G-16"},{"path":"https://didiermurillof.github.io/FielDHub/articles/alpha_lattice.html","id":"access-to-alpha-object","dir":"Articles","previous_headings":"2. Using the FielDHub function: alpha_lattice()","what":"Access to alpha object","title":"Alpha Lattice Design","text":"function alpha_lattice() returns list consisting information displayed output tabs FielDHub app: design information, plot layout, plot numbering, entries list, field book. accessible $ operator, .e.Β alpha$layoutRandom alpha$fieldBook. alpha$fieldBook list containing information every plot field, information location plot treatment plot. seen output , field book columns ID, LOCATION, PLOT, REP, IBLOCK, UNIT, ENTRY, TREATMENT.","code":"field_book <- alpha$fieldBook head(alpha$fieldBook, 10) ID LOCATION PLOT REP IBLOCK UNIT ENTRY TREATMENT 1 1 FARGO 101 1 1 1 15 G-15 2 2 FARGO 102 1 1 2 8 G-8 3 3 FARGO 103 1 1 3 51 G-51 4 4 FARGO 104 1 1 4 54 G-54 5 5 FARGO 105 1 1 5 4 G-4 6 6 FARGO 106 1 2 1 50 G-50 7 7 FARGO 107 1 2 2 40 G-40 8 8 FARGO 108 1 2 3 42 G-42 9 9 FARGO 109 1 2 4 22 G-22 10 10 FARGO 110 1 2 5 16 G-16"},{"path":"https://didiermurillof.github.io/FielDHub/articles/alpha_lattice.html","id":"plot-the-field-layout","dir":"Articles","previous_headings":"2. Using the FielDHub function: alpha_lattice()","what":"Plot the field layout","title":"Alpha Lattice Design","text":"plotting layout function coordinates ROW COLUMN, can use generic function plot() follows,","code":"plot(alpha)"},{"path":"https://didiermurillof.github.io/FielDHub/articles/crd.html","id":"using-the-fieldhub-shiny-app","dir":"Articles","previous_headings":"","what":"1. Using the FielDHub Shiny App","title":"Completely Randomized Design","text":"launch app need run either app running, go Designs > Completely Randomized Design (CRD) , follow following steps show generate kind design example 15 treatments 6 reps .","code":"FielDHub::run_app() library(FielDHub) run_app()"},{"path":"https://didiermurillof.github.io/FielDHub/articles/crd.html","id":"inputs","dir":"Articles","previous_headings":"","what":"Inputs","title":"Completely Randomized Design","text":"selection , means app going generate synthetic data entries names treatment/genotypes based user inputs. selection Yes, entries list must fulfill specific format must .csv file. file must single column TREATMENT, containing list unique names identify treatment. Duplicate values allowed, entries must unique. following table, show example entries list format. example entry list ten treatments. Input number treatments Input # Treatments box, 15 case. Select number replications treatments Input # Full Reps box. Set 6. Select serpentine cartesian Plot Order Layout. example use default cartesian layout. Enter starting plot number Starting Plot Number box. Set 101. Enter name location experiment Input Location box. completely randomized design can run single location time. Set FARGO. ensure randomizations consistent across sessions, can set random seed box labeled random seed. example, set 1236. entered information experiment left side panel, click Run! button run design.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/crd.html","id":"outputs","dir":"Articles","previous_headings":"","what":"Outputs","title":"Completely Randomized Design","text":"run completely randomized design FielDHub, several ways display information contained field book.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/crd.html","id":"field-layout","dir":"Articles","previous_headings":"Outputs","what":"Field Layout","title":"Completely Randomized Design","text":"first click run button completely randomized design, FielDHub displays Field Layout tab, shows entries arrangement field. box display, can change layout field. can also display heatmap field changing Type Plot Heatmap. view heatmap, must first simulate experiment described field Simulate! button. pop-window appear can enter variable want simulate along minimum maximum values.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/crd.html","id":"field-book","dir":"Articles","previous_headings":"Outputs","what":"Field Book","title":"Completely Randomized Design","text":"Field Book displays information experimental design table format. contains specific plot number row column address entry, well corresponding treatment plot. table searchable, can filter data relevant columns. simulated data heatmap, additional column variable appears Field Book.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/crd.html","id":"using-the-fieldhub-function-crd","dir":"Articles","previous_headings":"","what":"2. Using the FielDHub function: CRD()","title":"Completely Randomized Design","text":"can run design function FielDHub package, CRD(). can enter information describing design like :","code":"crd <- CRD( t = 15, reps = 6, plotNumber = 101, locationName = \"FARGO\", seed = 1236 )"},{"path":"https://didiermurillof.github.io/FielDHub/articles/crd.html","id":"details-on-the-inputs-entered-in-crd-above","dir":"Articles","previous_headings":"2. Using the FielDHub function: CRD()","what":"Details on the inputs entered in CRD() above","title":"Completely Randomized Design","text":"description inputs used generate design, t = 15 number treatments. reps = 6 number replications treatment. plotNumber = 101 starting plot number. locationNames = \"FARGO\" optional name location. seed = 1234 random seed replicate identical randomizations.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/crd.html","id":"print-crd-output","dir":"Articles","previous_headings":"2. Using the FielDHub function: CRD()","what":"Print crd output","title":"Completely Randomized Design","text":"print summary information object crd, can use generic function print().","code":"print(crd) Completely Randomized Design (CRD) Information on the design parameters: List of 5 $ numberofTreatments: num 15 $ treatments : chr [1:15] \"T1\" \"T2\" \"T3\" \"T4\" ... $ Reps : num 6 $ locationName : chr \"FARGO\" $ seed : num 1236 10 First observations of the data frame with the CRD field book: ID LOCATION PLOT REP TREATMENT 1 1 FARGO 101 4 T9 2 2 FARGO 102 4 T12 3 3 FARGO 103 2 T5 4 4 FARGO 104 3 T9 5 5 FARGO 105 6 T13 6 6 FARGO 106 5 T10 7 7 FARGO 107 5 T5 8 8 FARGO 108 2 T10 9 9 FARGO 109 2 T8 10 10 FARGO 110 3 T10"},{"path":"https://didiermurillof.github.io/FielDHub/articles/crd.html","id":"access-to-crd-output","dir":"Articles","previous_headings":"2. Using the FielDHub function: CRD()","what":"Access to crd output","title":"Completely Randomized Design","text":"CRD() function returns list consisting information displayed output tabs FielDHub app: design information, plot layout, plot numbering, entries list, field book. accessible $ operator, .e.Β crd$layoutRandom crd$fieldBook. crd$fieldBook list containing information every plot field, information location plot treatment plot. seen output , field book columns ID, LOCATION, PLOT, REP, IBLOCK, UNIT, ENTRY, TREATMENT.","code":"field_book <- crd$fieldBook head(crd$fieldBook, 10) ID LOCATION PLOT REP TREATMENT 1 1 FARGO 101 4 T9 2 2 FARGO 102 4 T12 3 3 FARGO 103 2 T5 4 4 FARGO 104 3 T9 5 5 FARGO 105 6 T13 6 6 FARGO 106 5 T10 7 7 FARGO 107 5 T5 8 8 FARGO 108 2 T10 9 9 FARGO 109 2 T8 10 10 FARGO 110 3 T10"},{"path":"https://didiermurillof.github.io/FielDHub/articles/crd.html","id":"plot-the-field-layout","dir":"Articles","previous_headings":"2. Using the FielDHub function: CRD()","what":"Plot the field layout","title":"Completely Randomized Design","text":"plotting layout function coordinates ROW COLUMN, can use generic function plot() follow,","code":"plot(crd)"},{"path":"https://didiermurillof.github.io/FielDHub/articles/diagonal_arrangement.html","id":"single-unreplicated-diagonal-arrangement-design","dir":"Articles","previous_headings":"","what":"Single Unreplicated Diagonal Arrangement Design","title":"Unreplicated Diagonal Arrangement Design","text":"vignette shows generate single multiple unreplicated diagonal arrangement designs using FielDHub Shiny App scripting function diagonal_arrangement() FielDHub R package.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/diagonal_arrangement.html","id":"overview","dir":"Articles","previous_headings":"","what":"Overview","title":"Unreplicated Diagonal Arrangement Design","text":"experiments, insufficient seed quantity field space conduct trials large numbers genotypes, plant breeders must use unreplicated partially replicated experimental designs, like unreplicated designs checks allocated systematic diagonal distribution(Clarke Stefanova 2011). cases, experiment split blocks specified size. allows breeders design field contains multiple different experiments, example, plants various stages maturity. FielDHub includes function run experimental designs, well tabs single multiple diagonal arrangement FielDHub app.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/diagonal_arrangement.html","id":"use-case","dir":"Articles","previous_headings":"","what":"Use Case","title":"Unreplicated Diagonal Arrangement Design","text":"Suppose plant breeding project needs identify superior entries barley. project, preliminary yield trial (PYT) carried 300 genotypes tested one experiment one location unreplicated design. experiment lying field containing 15 rows 22 columns plots. addition, 5 checks included systematic diagonal arrangement across field fill 30 plots representing 9.1% total number experimental plots.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/diagonal_arrangement.html","id":"running-the-shiny-app","dir":"Articles","previous_headings":"","what":"Running the Shiny App","title":"Unreplicated Diagonal Arrangement Design","text":"launch app need run either ","code":"FielDHub::run_app() library(FielDHub) run_app()"},{"path":"https://didiermurillof.github.io/FielDHub/articles/diagonal_arrangement.html","id":"using-the-fieldhub-shiny-app","dir":"Articles","previous_headings":"","what":"1. Using the FielDHub Shiny App","title":"Unreplicated Diagonal Arrangement Design","text":"app running, go Unreplicated Designs > Single Diagonal Arrangement , follow following steps show generate single unreplicated diagonal arrangement design.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/diagonal_arrangement.html","id":"inputs","dir":"Articles","previous_headings":"","what":"Inputs","title":"Unreplicated Diagonal Arrangement Design","text":"selection , means app going generate synthetic data entries names treatment/genotypes based user inputs. selection Yes, entries list must fulfill specific format must .csv file. file must columns ENTRY NAME. ENTRY column must unique integer number entry treatment/genotype. column NAME must unique name identifies treatment/genotype. ENTRY NAME must unique, duplicates allowed. following table, show example entries list format. example entry list 4 checks 8 treatments/genotypes. crucial allocate checks top part file. Enter number entries/treatments Input # Entries box, 300 case. Select 5 drop-Input # Checks box. Since want run experiment 1 location, set Input # Locations 1. Select serpentine cartesian Plot Order Layout. example use serpentine layout. ensure randomizations consistent across sessions, can set random seed box labeled random seed. instance, set 16. Enter name experiment Input Experiment Name box. example, PYT_BARLEY_2022. Enter starting plot number Starting Plot Number box. experiment want plot start 1001. Enter name site/location Input Location box. experiment set site FARGO. case users run experiment multiple locations, name location must enter separate comma, example: FARGO, CASSELTON, MINOT. entered information experiment left side panel, click Run! button run design. prompted select dimensions field list options drop-middle screen box labeled Select dimensions field. case, select 15 x 22. Click Randomize! button randomize experiment set field dimensions see output plots. change inputs left side panel running experiment initially, click Run Randomize buttons , re-run new inputs.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/diagonal_arrangement.html","id":"outputs","dir":"Articles","previous_headings":"","what":"Outputs","title":"Unreplicated Diagonal Arrangement Design","text":"run single diagonal arrangement FielDHub set dimensions field, several ways display information contained field book. first tab, Expt Design Info, shows option change dimensions field re-randomize, well reference guide experiment design.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/diagonal_arrangement.html","id":"input-data","dir":"Articles","previous_headings":"Outputs","what":"Input Data","title":"Unreplicated Diagonal Arrangement Design","text":"second tab, Input Data, can see entries randomization list generated inputs, well table checks number times appear field.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/diagonal_arrangement.html","id":"randomized-field","dir":"Articles","previous_headings":"Outputs","what":"Randomized Field","title":"Unreplicated Diagonal Arrangement Design","text":"Randomized Field tab displays graphical representation randomization entries field specified dimensions. checks colored uniquely, showing number times distributed throughout field. display includes numbered labels rows columns. can copy field table save directly Excel file Copy Excel buttons top. Choose % Checks: drop-box, users can play different options total amount checks field.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/diagonal_arrangement.html","id":"plot-number-field","dir":"Articles","previous_headings":"Outputs","what":"Plot Number Field","title":"Unreplicated Diagonal Arrangement Design","text":"Plot Number Field tab, table display field plots numbered according Plot Order Layout specified, either serpentine cartesian. can see corresponding entries plot number field book. Like Randomized Field tab, can copy table save Excel file Copy Excel buttons.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/diagonal_arrangement.html","id":"field-book","dir":"Articles","previous_headings":"Outputs","what":"Field Book","title":"Unreplicated Diagonal Arrangement Design","text":"Field Book displays information experimental design table format. contains specific plot number row column address entry, well corresponding treatment plot. table searchable, can filter data relevant columns.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/diagonal_arrangement.html","id":"using-the-fieldhub-function-diagonal_arrangement","dir":"Articles","previous_headings":"","what":"2. Using the FielDHub function: diagonal_arrangement()","title":"Unreplicated Diagonal Arrangement Design","text":"can run design function FielDHub package, diagonal_arrangement(). First, need load FielDHub package typing, , can enter information describing design like :","code":"library(FielDHub) single_diag <- diagonal_arrangement( nrows = 15, ncols = 22, lines = 300, checks = 5, l = 1, plotNumber = 1, exptName = \"PYT_BARLEY_2022\", locationNames = \"FARGO\", seed = 16, )"},{"path":"https://didiermurillof.github.io/FielDHub/articles/diagonal_arrangement.html","id":"details-on-the-inputs-entered-in-diagonal_arrangement-above","dir":"Articles","previous_headings":"2. Using the FielDHub function: diagonal_arrangement()","what":"Details on the inputs entered in diagonal_arrangement() above:","title":"Unreplicated Diagonal Arrangement Design","text":"nrows = 15 number columns field. ncols = 22 number rows field. lines = 300 number genotypes. checks = 5 number checks. l = 1 number locations. plotNumber = 1 starting plot number. exptName = \"PYT_BARLEY_2022\" optional name experiment locationNames = \"FARGO\" optional name location. seed = 16 random seed replicate identical randomizations.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/diagonal_arrangement.html","id":"print-single_diag-object","dir":"Articles","previous_headings":"2. Using the FielDHub function: diagonal_arrangement()","what":"Print single_diag object","title":"Unreplicated Diagonal Arrangement Design","text":"print summary information object single_diag, can use generic function print().","code":"print(single_diag) Un-replicated Diagonal Arrangement Design Information on the design parameters: List of 11 $ rows : num 15 $ columns : num 22 $ treatments : int 300 $ checks : int 5 $ entry_checks :List of 1 ..$ : int [1:5] 1 2 3 4 5 $ rep_checks :List of 1 ..$ : num [1:5] 6 6 6 6 6 $ locations : num 1 $ planter : chr \"serpentine\" $ percent_checks: chr \"9.1%\" $ fillers : num 0 $ seed : num 16 10 First observations of the data frame with the diagonal_arrangement field book: ID EXPT LOCATION YEAR PLOT ROW COLUMN CHECKS ENTRY TREATMENT 1 1 PYT_BARLEY_2022 FARGO 2024 1 1 1 0 152 Gen-152 2 2 PYT_BARLEY_2022 FARGO 2024 2 1 2 0 38 Gen-38 3 3 PYT_BARLEY_2022 FARGO 2024 3 1 3 0 285 Gen-285 4 4 PYT_BARLEY_2022 FARGO 2024 4 1 4 0 226 Gen-226 5 5 PYT_BARLEY_2022 FARGO 2024 5 1 5 0 215 Gen-215 6 6 PYT_BARLEY_2022 FARGO 2024 6 1 6 0 272 Gen-272 7 7 PYT_BARLEY_2022 FARGO 2024 7 1 7 0 91 Gen-91 8 8 PYT_BARLEY_2022 FARGO 2024 8 1 8 0 126 Gen-126 9 9 PYT_BARLEY_2022 FARGO 2024 9 1 9 0 289 Gen-289 10 10 PYT_BARLEY_2022 FARGO 2024 10 1 10 0 248 Gen-248"},{"path":"https://didiermurillof.github.io/FielDHub/articles/diagonal_arrangement.html","id":"access-to-single_diag-object","dir":"Articles","previous_headings":"2. Using the FielDHub function: diagonal_arrangement()","what":"Access to single_diag object","title":"Unreplicated Diagonal Arrangement Design","text":"function diagonal_arrangement() returns list consisting information displayed output tabs FielDHub app: design information, plot layout, plot numbering, entries list, field book. accessible $ operator, .e.Β single_diag$layoutRandom single_diag$fieldBook. single_diag$fieldBook data frame containing information every plot field, information location plot treatment plot. seen output , field book columns ID, EXPT, LOCATION, YEAR, PLOT, ROW, COLUMN, CHECKS, ENTRY, TREATMENT. Let us see first 10 rows field book experiment.","code":"field_book <- single_diag$fieldBook head(field_book, 10) ID EXPT LOCATION YEAR PLOT ROW COLUMN CHECKS ENTRY TREATMENT 1 1 PYT_BARLEY_2022 FARGO 2024 1 1 1 0 152 Gen-152 2 2 PYT_BARLEY_2022 FARGO 2024 2 1 2 0 38 Gen-38 3 3 PYT_BARLEY_2022 FARGO 2024 3 1 3 0 285 Gen-285 4 4 PYT_BARLEY_2022 FARGO 2024 4 1 4 0 226 Gen-226 5 5 PYT_BARLEY_2022 FARGO 2024 5 1 5 0 215 Gen-215 6 6 PYT_BARLEY_2022 FARGO 2024 6 1 6 0 272 Gen-272 7 7 PYT_BARLEY_2022 FARGO 2024 7 1 7 0 91 Gen-91 8 8 PYT_BARLEY_2022 FARGO 2024 8 1 8 0 126 Gen-126 9 9 PYT_BARLEY_2022 FARGO 2024 9 1 9 0 289 Gen-289 10 10 PYT_BARLEY_2022 FARGO 2024 10 1 10 0 248 Gen-248"},{"path":"https://didiermurillof.github.io/FielDHub/articles/diagonal_arrangement.html","id":"plot-field-layout","dir":"Articles","previous_headings":"2. Using the FielDHub function: diagonal_arrangement()","what":"Plot field layout","title":"Unreplicated Diagonal Arrangement Design","text":"plotting layout function coordinates ROW COLUMN field book object can use generic function plot() follow, figure shows map experiment randomized single unreplicated diagonal arrangement design. Gray plots represent unreplicated treatments, distinctively colored check plots replicated throughout field systematic diagonal arrangement.","code":"plot(single_diag)"},{"path":"https://didiermurillof.github.io/FielDHub/articles/diagonal_arrangement.html","id":"multiple-unreplicated-diagonal-arrangement-design","dir":"Articles","previous_headings":"","what":"Multiple Unreplicated Diagonal Arrangement Design","title":"Unreplicated Diagonal Arrangement Design","text":"Now, show generate kind unreplicated design multiple experiments field.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/diagonal_arrangement.html","id":"use-case-1","dir":"Articles","previous_headings":"","what":"Use Case","title":"Unreplicated Diagonal Arrangement Design","text":"plant breeding project needs test 300 genotypes divided among three different experiments amounts 100, 120, 80 respectively. experiment represents different stages maturity. 3 experiments lying field containing 15 rows 22 columns plots. addition, 5 checks included systematic diagonal arrangement across experiments fill 30 plots representing 9.1% total number experimental plots. FielDHub can perform randomization design problem explained . can solved either app diagonal_arrangement() function.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/diagonal_arrangement.html","id":"using-the-fieldhub-shiny-app-1","dir":"Articles","previous_headings":"","what":"1. Using the FielDHub Shiny App","title":"Unreplicated Diagonal Arrangement Design","text":"generate multiple unreplicated diagonal arrangement design using FielDHub app: First, go Unreplicated Designs > Multiple Diagonal Arrangement , follow following steps show generate kind design.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/diagonal_arrangement.html","id":"inputs-1","dir":"Articles","previous_headings":"","what":"Inputs","title":"Unreplicated Diagonal Arrangement Design","text":"selection , means app going generate synthetic data entries names treatment/genotypes based user inputs. selection Yes, entries list must fulfill specific format must .csv file. file must columns ENTRY NAME. ENTRY column must unique entry integer number treatment/genotype. column NAME must unique name identifies treatment/genotype. ENTRY NAME must unique, duplicates allowed. following table, show example entries list format. example entry list four checks 8 treatments/genotypes. crucial allocate checks top part file. Note: wish create multiple blocks different sizes imported entries list, example, block size 80, 90, 100 plots, FielDHub read imported entries list checks, 80 entries first block, 90 entries second block, 100 entries last block. Select checkbox option Use entries across experiments purpose make replications instead testing different experiments. Checking option requires size blocks. example, testing 100 treatments across 3 blocks require set 300 Input # Entries 100, 100, 100 input Input # Entries per Expt. case keep unchecked option. Enter total number entries/treatments Input # Entries box, 300 case. Enter number entries/treatments experiment separate comma Input # Entries per Expt box, 100, 120, 80 case. Select 5 drop-Input # Checks box. Since want run experiment 1 location, set Input # Locations 1. Select Row Column Blocks Layout:. example set Row experiments/blocks layout. Select serpentine cartesian Plot Order Layout. example set serpentine layout. Enter starting plot number experiment Starting Plot Number box. experiment want plot start 1, 1001, 2001 experiment. app also allows setting one number experiments. example, plot number start 10. Enter name experiment Input Experiment Name box. example, MATURITY1, MATURITY2, MATURITY3. ensure randomization consistent across sessions, can set random seed box labeled random seed. instance, set 17. Enter name site/location Input Location box. experiment set site FARGO. case users run experiment multiple locations, name location must enter separate comma, example: FARGO, CASSELTON, MINOT. entered information experiment left side panel, click Run! button run design. prompted select dimensions field list options drop-middle screen box labeled Select dimensions field. case, select 15 x 22. Click Randomize! button randomize experiment set field dimensions see output plots. change inputs left side panel running experiment initially, click Run Randomize buttons , re-run new inputs.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/diagonal_arrangement.html","id":"outputs-1","dir":"Articles","previous_headings":"","what":"Outputs","title":"Unreplicated Diagonal Arrangement Design","text":"run single diagonal arrangement FielDHub set dimensions field, several ways display information contained field book. first tab, Expt Design Info, shows option change dimensions field re-randomize, well reference guide experiment design.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/diagonal_arrangement.html","id":"input-data-1","dir":"Articles","previous_headings":"Outputs","what":"Input Data","title":"Unreplicated Diagonal Arrangement Design","text":"second tab, Input Data, can see entries randomization list generated inputs, well table checks number times appear field.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/diagonal_arrangement.html","id":"randomized-field-1","dir":"Articles","previous_headings":"Outputs","what":"Randomized Field","title":"Unreplicated Diagonal Arrangement Design","text":"Randomized Field tab displays graphical representation randomization entries field specified dimensions. checks colored uniquely, showing number times distributed throughout field. display includes numbered labels rows columns. can copy field table save directly Excel file Copy Excel buttons top. tab Choose % Checks: box users can play different options total amount checks field.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/diagonal_arrangement.html","id":"plot-number-field-1","dir":"Articles","previous_headings":"Outputs","what":"Plot Number Field","title":"Unreplicated Diagonal Arrangement Design","text":"Plot Number Field tab, table display field plots numbered according Plot Order Layout specified, either serpentine cartesian. can see corresponding entries plot number field book. Like Randomized field tab, can copy table save Excel file Copy Excel buttons.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/diagonal_arrangement.html","id":"field-book-1","dir":"Articles","previous_headings":"Outputs","what":"Field Book","title":"Unreplicated Diagonal Arrangement Design","text":"Field Book displays information experimental design table format. contains specific plot number row column address entry, well corresponding treatment plot. table searchable, can filter data relevant columns.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/diagonal_arrangement.html","id":"using-the-fieldhub-function-diagonal_arrangement-1","dir":"Articles","previous_headings":"","what":"2. Using the FielDHub function: diagonal_arrangement()","title":"Unreplicated Diagonal Arrangement Design","text":"variation single diagonal arrangement included diagonal_arrangement() function multiple diagonal arrangement, experiment split blocks specified size.","code":"multi_diag <- diagonal_arrangement( nrows = 15, ncols = 22, lines = 300, kindExpt = \"DBUDC\", blocks = c(100,120,80), checks = 5, l = 1, plotNumber = c(1, 1001, 2001), exptName = c(\"MATURITY1\", \"MATURITY2\", \"MATURITY3\"), locationNames = \"FARGO\", seed = 17 )"},{"path":"https://didiermurillof.github.io/FielDHub/articles/diagonal_arrangement.html","id":"details-on-the-inputs-entered-in-diagonal_arrangement-above-1","dir":"Articles","previous_headings":"2. Using the FielDHub function: diagonal_arrangement()","what":"Details on the inputs entered in diagonal_arrangement() above:","title":"Unreplicated Diagonal Arrangement Design","text":"description inputs used generate design, nrows = 15 number columns field. ncols = 22 number rows field. lines = 300 number genotypes. kindExpt = \"DBUDC\" option randomize multiple experiments blocks = c(100,120,80) blocks multiple arrangement. checks = 5 number checks. l = 1 number locations. plotNumber = c(1, 1001, 2001) starting plot number experiment. just one number well. exptName = c(\"MATURITY1\", \"MATURITY2\", \"MATURITY3\") optional name experiment. locationNames = \"FARGO\" optional name location. seed = 17 random seed replicate identical randomizations.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/diagonal_arrangement.html","id":"print-multi_diag-object","dir":"Articles","previous_headings":"2. Using the FielDHub function: diagonal_arrangement()","what":"Print multi_diag object","title":"Unreplicated Diagonal Arrangement Design","text":"printing summary information object multi_diag can use generic function print()","code":"print(multi_diag) Un-replicated Diagonal Arrangement Design Information on the design parameters: List of 11 $ rows : num 15 $ columns : num 22 $ treatments : num [1:3] 100 120 80 $ checks : int 5 $ entry_checks :List of 1 ..$ : int [1:5] 1 2 3 4 5 $ rep_checks :List of 1 ..$ : num [1:5] 7 5 7 5 6 $ locations : num 1 $ planter : chr \"serpentine\" $ percent_checks: chr \"9.1%\" $ fillers : num 0 $ seed : num 17 10 First observations of the data frame with the diagonal_arrangement field book: ID EXPT LOCATION YEAR PLOT ROW COLUMN CHECKS ENTRY TREATMENT 1 1 MATURITY1 FARGO 2024 1 1 1 0 104 Gen-104 2 2 MATURITY1 FARGO 2024 2 1 2 0 65 Gen-65 3 3 MATURITY1 FARGO 2024 3 1 3 0 70 Gen-70 4 4 MATURITY1 FARGO 2024 4 1 4 0 8 Gen-8 5 5 MATURITY1 FARGO 2024 5 1 5 0 51 Gen-51 6 6 MATURITY1 FARGO 2024 6 1 6 0 17 Gen-17 7 7 MATURITY1 FARGO 2024 7 1 7 0 11 Gen-11 8 8 MATURITY1 FARGO 2024 8 1 8 0 6 Gen-6 9 9 MATURITY1 FARGO 2024 9 1 9 0 53 Gen-53 10 10 MATURITY1 FARGO 2024 10 1 10 0 50 Gen-50"},{"path":"https://didiermurillof.github.io/FielDHub/articles/diagonal_arrangement.html","id":"access-to-multi_diag-object","dir":"Articles","previous_headings":"2. Using the FielDHub function: diagonal_arrangement()","what":"Access to multi_diag object","title":"Unreplicated Diagonal Arrangement Design","text":"function diagonal_arrangement() returns list consisting information displayed output tabs FielDHub app: design information, plot layout, plot numbering, entries list, field book. accessible $ operator, .e.Β multi_diag$layoutRandom multi_diag$fieldBook. multi_diag$fieldBook data frame containing information every plot field, information location plot treatment plot. seen output , field book columns ID, EXPT, LOCATION, YEAR, PLOT, ROW, COLUMN, CHECKS, ENTRY, TREATMENT. Let us see first 10 rows field book experiment.","code":"field_book <- multi_diag$fieldBook head(field_book, 10) ID EXPT LOCATION YEAR PLOT ROW COLUMN CHECKS ENTRY TREATMENT 1 1 MATURITY1 FARGO 2024 1 1 1 0 104 Gen-104 2 2 MATURITY1 FARGO 2024 2 1 2 0 65 Gen-65 3 3 MATURITY1 FARGO 2024 3 1 3 0 70 Gen-70 4 4 MATURITY1 FARGO 2024 4 1 4 0 8 Gen-8 5 5 MATURITY1 FARGO 2024 5 1 5 0 51 Gen-51 6 6 MATURITY1 FARGO 2024 6 1 6 0 17 Gen-17 7 7 MATURITY1 FARGO 2024 7 1 7 0 11 Gen-11 8 8 MATURITY1 FARGO 2024 8 1 8 0 6 Gen-6 9 9 MATURITY1 FARGO 2024 9 1 9 0 53 Gen-53 10 10 MATURITY1 FARGO 2024 10 1 10 0 50 Gen-50"},{"path":"https://didiermurillof.github.io/FielDHub/articles/diagonal_arrangement.html","id":"plot-field-layout-1","dir":"Articles","previous_headings":"2. Using the FielDHub function: diagonal_arrangement()","what":"Plot field layout","title":"Unreplicated Diagonal Arrangement Design","text":"plotting layout function coordinates ROW COLUMN field book object can use generic function plot() follow, figure shows map experiment randomized multiple unreplicated diagonal arrangement design. Gray, salmon, pink shade blocks unreplicated experiments, distinctively colored check plots replicated throughout field systematic diagonal arrangement.","code":"plot(multi_diag)"},{"path":[]},{"path":"https://didiermurillof.github.io/FielDHub/articles/full_factorial.html","id":"using-the-fieldhub-shiny-app","dir":"Articles","previous_headings":"","what":"1. Using the FielDHub Shiny App","title":"Full Factorial Design","text":"launch app need run either app running, go Designs > Full Factorial Designs , follow following steps show generate kind design example set 3 treatments levels 3, 3, 2 . run experiment 3 times.","code":"FielDHub::run_app() library(FielDHub) run_app()"},{"path":"https://didiermurillof.github.io/FielDHub/articles/full_factorial.html","id":"inputs","dir":"Articles","previous_headings":"","what":"Inputs","title":"Full Factorial Design","text":"selection , means app going generate synthetic data entries names treatment based user inputs. selection Yes, entries list must fulfill specific format must .csv file. file must two columns: FACTORS LEVEL. Containing list unique names identify treatment level. Duplicate values allowed, entries must unique. following table, show example entries list format. example entry list three treatments/factors, 3, 3 2 levels . Choose whether use factorial design RCBD CRD Select Factorial Design Type box. Set RCBD. Set number entries factor comma separated list Input # Entries Factor box. want example experiment 3 factors 3, 3, 2 levels respectively, enter 3, 3, 2 box. Set number replications squares Input # Full Reps box. Set 3. Enter number locations Input # Locations. Set 1. Enter starting plot number Starting Plot Number box. experiment multiple locations, must enter comma separated list numbers length number locations input valid. case, set 101. Optionally, may enter name location experiment Input Location box. Select serpentine cartesian Plot Order Layout. example use default serpentine layout. designs, can set random seed box labeled random seed. example, set 1239. entered information experiment left side panel, click Run! button run design.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/full_factorial.html","id":"outputs","dir":"Articles","previous_headings":"","what":"Outputs","title":"Full Factorial Design","text":"run full factorial design FielDHub, several ways display information contained field book.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/full_factorial.html","id":"field-layout","dir":"Articles","previous_headings":"Outputs","what":"Field Layout","title":"Full Factorial Design","text":"first click run button full factorial design, FielDHub displays Field Layout tab, shows entries arrangement field. box display, can change layout field change location displayed. can also display heatmap field changing Type Plot Heatmap. view heatmap, must first simulate experiment described field Simulate! button. pop-window appear can enter variable want simulate along minimum maximum values.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/full_factorial.html","id":"field-book","dir":"Articles","previous_headings":"Outputs","what":"Field Book","title":"Full Factorial Design","text":"Field Book displays information experimental design table format. contains specific plot number row column address entry, well corresponding treatment plot. table searchable, can filter data relevant columns. simulated data heatmap, additional column variable appears Field Book.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/full_factorial.html","id":"using-the-fieldhub-function-full_factorial","dir":"Articles","previous_headings":"","what":"2. Using the FielDHub function: full_factorial()","title":"Full Factorial Design","text":"can run design function FielDHub package, full_factorial(). First, need load FielDHub package typing, , can enter information describing design like :","code":"library(FielDHub) factorial <- full_factorial( setfactors = c(3,3,2), reps = 3, l = 1, type = 2, plotNumber = 101, planter = \"serpentine\", locationNames = \"FARGO\", seed = 1239 )"},{"path":"https://didiermurillof.github.io/FielDHub/articles/full_factorial.html","id":"details-on-the-inputs-entered-in-full_factorial-above","dir":"Articles","previous_headings":"2. Using the FielDHub function: full_factorial()","what":"Details on the inputs entered in full_factorial() above","title":"Full Factorial Design","text":"description inputs used generate design, setfactors = c(3,3,2) levels factor. reps = 3 number replications treatment. l = 1 number locations. type = 2 means CRD RCBD, 1 2 respectively. plotNumber = 101 starting plot number. planter = \"serpentine\" order layout. locationNames = \"FARGO\" optional name location. seed = 1239 random seed replicate identical randomizations.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/full_factorial.html","id":"print-factorial-object","dir":"Articles","previous_headings":"2. Using the FielDHub function: full_factorial()","what":"Print factorial object","title":"Full Factorial Design","text":"","code":"print(factorial) Full Factorial Design Information on the design parameters: List of 9 $ factors : chr [1:3] \"A\" \"B\" \"C\" $ levels : int [1:8] 0 1 2 0 1 2 0 1 $ runs : int 18 $ all_treatments :'data.frame': 18 obs. of 3 variables: ..$ A: int [1:18] 0 1 2 0 1 2 0 1 2 0 ... ..$ B: int [1:18] 0 0 0 1 1 1 2 2 2 0 ... ..$ C: int [1:18] 0 0 0 0 0 0 0 0 0 1 ... $ reps : num 3 $ locations : num 1 $ location_names : chr \"FARGO\" $ kind : chr \"RCBD\" $ levels_each_factor: num [1:3] 3 3 2 10 First observations of the data frame with the full_factorial field book: ID LOCATION PLOT REP FACTOR_A FACTOR_B FACTOR_C TRT_COMB 1 1 FARGO 101 1 0 1 0 0*1*0 2 2 FARGO 102 1 1 1 0 1*1*0 3 3 FARGO 103 1 2 1 0 2*1*0 4 4 FARGO 104 1 2 1 1 2*1*1 5 5 FARGO 105 1 2 2 0 2*2*0 6 6 FARGO 106 1 1 0 1 1*0*1 7 7 FARGO 107 1 0 0 1 0*0*1 8 8 FARGO 108 1 1 2 0 1*2*0 9 9 FARGO 109 1 0 2 0 0*2*0 10 10 FARGO 110 1 0 1 1 0*1*1"},{"path":"https://didiermurillof.github.io/FielDHub/articles/full_factorial.html","id":"access-to-factorial-object","dir":"Articles","previous_headings":"2. Using the FielDHub function: full_factorial()","what":"Access to factorial object","title":"Full Factorial Design","text":"full_factorial() function returns list consisting information displayed output tabs FielDHub app: design information, plot layout, plot numbering, entries list, field book. accessible $ operator, .e.Β factorial$layoutRandom factorial$fieldBook. factorial$fieldBook list containing information every plot field, information location plot treatment plot. seen output , field book columns ID, LOCATION, PLOT, REP, TRT_COMB, columns factor individually.","code":"field_book <- factorial$fieldBook head(factorial$fieldBook, 10) ID LOCATION PLOT REP FACTOR_A FACTOR_B FACTOR_C TRT_COMB 1 1 FARGO 101 1 0 1 0 0*1*0 2 2 FARGO 102 1 1 1 0 1*1*0 3 3 FARGO 103 1 2 1 0 2*1*0 4 4 FARGO 104 1 2 1 1 2*1*1 5 5 FARGO 105 1 2 2 0 2*2*0 6 6 FARGO 106 1 1 0 1 1*0*1 7 7 FARGO 107 1 0 0 1 0*0*1 8 8 FARGO 108 1 1 2 0 1*2*0 9 9 FARGO 109 1 0 2 0 0*2*0 10 10 FARGO 110 1 0 1 1 0*1*1"},{"path":"https://didiermurillof.github.io/FielDHub/articles/full_factorial.html","id":"plot-the-field-layout","dir":"Articles","previous_headings":"2. Using the FielDHub function: full_factorial()","what":"Plot the field layout","title":"Full Factorial Design","text":"plotting layout function coordinates ROW COLUMN, can use generic function plot() follow,","code":"plot(factorial)"},{"path":"https://didiermurillof.github.io/FielDHub/articles/incomplete_blocks.html","id":"using-the-fieldhub-shiny-app","dir":"Articles","previous_headings":"","what":"1. Using the FielDHub Shiny App","title":"Incomplete Block Design","text":"launch app need run either app running, go Designs > Incomplete Block Design (IBD) , follow following steps show generate kind design example 28 treatments 4 reps. run experiment just one location.","code":"FielDHub::run_app() library(FielDHub) run_app()"},{"path":"https://didiermurillof.github.io/FielDHub/articles/incomplete_blocks.html","id":"inputs","dir":"Articles","previous_headings":"","what":"Inputs","title":"Incomplete Block Design","text":"selection , means app going generate synthetic data entries names treatment/genotypes based user inputs. selection Yes, entries list must fulfill specific format must .csv file. file must columns ENTRY NAME. ENTRY column must unique entry integer number treatment/genotype. column NAME must unique name identifies treatment/genotype. ENTRY NAME must unique, duplicates allowed. following table, show example entries list format. example entry list 12 treatments. Input number treatments Input # Treatments box. alpha lattice design, number treatments must composite number. case, Set 28. Select number replications treatments Input # Full Reps box. Set 4. Set number plots incomplete block Input # Plots per IBlock box. Set 4. Enter number locations Input # Locations. run experiment single location, set 1. Select serpentine cartesian Plot Order Layout. example use default cartesian layout. Enter starting plot number Starting Plot Number box. experiment multiple locations, must enter comma separated list numbers length number locations input valid. Set 101. Enter name location experiment Input Location box. multiple locations, name must comma separated list. Set \"FARGO\". ensure randomizations consistent across sessions, can set random seed box labeled random seed. example, set 1243. entered information experiment left side panel, click Run! button run design.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/incomplete_blocks.html","id":"outputs","dir":"Articles","previous_headings":"","what":"Outputs","title":"Incomplete Block Design","text":"run incomplete block design FielDHub, several ways display information contained field book.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/incomplete_blocks.html","id":"field-layout","dir":"Articles","previous_headings":"Outputs","what":"Field Layout","title":"Incomplete Block Design","text":"first click run button incomplete block design, FielDHub displays Field Layout tab, shows entries arrangement field. box display, can change layout field. can also display heatmap field changing Type Plot Heatmap. view heatmap, must first simulate experiment described field Simulate! button. pop-window appear can enter variable want simulate along minimum maximum values.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/incomplete_blocks.html","id":"field-book","dir":"Articles","previous_headings":"Outputs","what":"Field Book","title":"Incomplete Block Design","text":"Field Book displays information experimental design table format. contains specific plot number row column address entry, well corresponding treatment plot. table searchable, can filter data relevant columns. simulated data heatmap, additional column variable appears Field Book.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/incomplete_blocks.html","id":"using-the-fieldhub-function-incomplete_blocks","dir":"Articles","previous_headings":"","what":"2. Using the FielDHub function: incomplete_blocks()","title":"Incomplete Block Design","text":"can run design function FielDHub package, incomplete_blocks(). First, need load FielDHub package typing, , can enter information describing design like :","code":"library(FielDHub) ibd <- incomplete_blocks( t = 28, r = 4, k = 4, l = 1, seed = 1243 )"},{"path":"https://didiermurillof.github.io/FielDHub/articles/incomplete_blocks.html","id":"details-on-the-inputs-entered-in-incomplete_blocks-above","dir":"Articles","previous_headings":"2. Using the FielDHub function: incomplete_blocks()","what":"Details on the inputs entered in incomplete_blocks() above","title":"Incomplete Block Design","text":"description inputs used generate design, t = 28 number treatments. r=4 number replicates. k = 4 number plots per incomplete block. l = 1 number locations plotNumber = 101 starting plot number. locationNames = \"FARGO\" optional name location. seed = 1243 random seed replicate identical randomizations.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/incomplete_blocks.html","id":"print-ibd-object","dir":"Articles","previous_headings":"2. Using the FielDHub function: incomplete_blocks()","what":"Print ibd object","title":"Incomplete Block Design","text":"","code":"print(ibd) Incomplete Blocks Design Efficiency of design: Level Blocks D-Efficiency A-Efficiency A-Bound 1 1 4 1.0000000 1.0000000 1.0000000 2 2 28 0.7603326 0.7431887 0.7470356 Information on the design parameters: List of 7 $ Reps : num 4 $ iBlocks : num 7 $ NumberTreatments: num 28 $ NumberLocations : num 1 $ Locations : int 1 $ seed : num 1243 $ lambda : num 0.444 10 First observations of the data frame with the incomplete_blocks field book: ID LOCATION PLOT REP IBLOCK UNIT ENTRY TREATMENT 1 1 1 101 1 1 1 6 G-6 2 2 1 102 1 1 2 11 G-11 3 3 1 103 1 1 3 15 G-15 4 4 1 104 1 1 4 23 G-23 5 5 1 105 1 2 1 17 G-17 6 6 1 106 1 2 2 7 G-7 7 7 1 107 1 2 3 28 G-28 8 8 1 108 1 2 4 13 G-13 9 9 1 109 1 3 1 12 G-12 10 10 1 110 1 3 2 20 G-20"},{"path":"https://didiermurillof.github.io/FielDHub/articles/incomplete_blocks.html","id":"access-to-ibd-object","dir":"Articles","previous_headings":"2. Using the FielDHub function: incomplete_blocks()","what":"Access to ibd object","title":"Incomplete Block Design","text":"incomplete_blocks() function returns list consisting information displayed output tabs FielDHub app: design information, plot layout, plot numbering, entries list, field book. accessible $ operator, .e.Β ibd$layoutRandom ibd$fieldBook. ibd$fieldBook list containing information every plot field, information location plot treatment plot. seen output , field book columns ID, LOCATION, PLOT, REP, IBLOCK, UNIT, ENTRY, TREATMENT.","code":"field_book <- ibd$fieldBook head(ibd$fieldBook, 10) ID LOCATION PLOT REP IBLOCK UNIT ENTRY TREATMENT 1 1 1 101 1 1 1 6 G-6 2 2 1 102 1 1 2 11 G-11 3 3 1 103 1 1 3 15 G-15 4 4 1 104 1 1 4 23 G-23 5 5 1 105 1 2 1 17 G-17 6 6 1 106 1 2 2 7 G-7 7 7 1 107 1 2 3 28 G-28 8 8 1 108 1 2 4 13 G-13 9 9 1 109 1 3 1 12 G-12 10 10 1 110 1 3 2 20 G-20"},{"path":"https://didiermurillof.github.io/FielDHub/articles/incomplete_blocks.html","id":"plot-the-field-layout","dir":"Articles","previous_headings":"2. Using the FielDHub function: incomplete_blocks()","what":"Plot the field layout","title":"Incomplete Block Design","text":"plotting layout function coordinates ROW COLUMN, can use generic function plot() follow,","code":"plot(ibd)"},{"path":"https://didiermurillof.github.io/FielDHub/articles/latin_square.html","id":"using-the-fieldhub-shiny-app","dir":"Articles","previous_headings":"","what":"1. Using the FielDHub Shiny App","title":"Latin Square Design","text":"launch app need run either app running, go Designs > Latin Square Design , follow following steps show generate kind design example 5 treatments 2 reps.","code":"FielDHub::run_app() library(FielDHub) run_app()"},{"path":"https://didiermurillof.github.io/FielDHub/articles/latin_square.html","id":"inputs","dir":"Articles","previous_headings":"","what":"Inputs","title":"Latin Square Design","text":"selection , means app going generate synthetic data entries names treatment based user inputs. selection Yes, entries list must fulfill specific format must .csv file. file must three columns: ROW, COLUMN TREATMENT. columns contain list unique names identify treatment. Duplicate values allowed, entries must unique. following table, show example entries list format. example entry list 5 treatments. Input number treatments Input # Treatments box. alpha lattice design, number treatments must composite number. Select number replications treatments Input # Full Reps box. number treatments number full reps set dimensions field. Select serpentine cartesian Plot Order Layout. example use default serpentine layout. Enter starting plot number Starting Plot Number box. experiment multiple locations, must enter comma separated list numbers length number locations input valid. Enter name location experiment Input Location box. completely randomized design can run single location time. ensure randomizations consistent across sessions, can set random seed box labeled random seed. example, set 123. entered information experiment left side panel, click Run! button run design.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/latin_square.html","id":"outputs","dir":"Articles","previous_headings":"","what":"Outputs","title":"Latin Square Design","text":"run Latin square design FielDHub, several ways display information contained field book.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/latin_square.html","id":"field-layout","dir":"Articles","previous_headings":"Outputs","what":"Field Layout","title":"Latin Square Design","text":"first click run button Latin square design, FielDHub displays Field Layout tab, shows entries arrangement field. box display, can change layout field. can also display heatmap field changing Type Plot Heatmap. view heatmap, must first simulate experiment described field Simulate! button. pop-window appear can enter variable want simulate along minimum maximum values.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/latin_square.html","id":"field-book","dir":"Articles","previous_headings":"Outputs","what":"Field Book","title":"Latin Square Design","text":"Field Book displays information experimental design table format. contains specific plot number row column address entry, well corresponding treatment plot. table searchable, can filter data relevant columns. simulated data heatmap, additional column variable appears field book.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/latin_square.html","id":"using-the-fieldhub-function-latin_square","dir":"Articles","previous_headings":"","what":"2. Using the FielDHub function: latin_square()","title":"Latin Square Design","text":"can run design function FielDHub package, latin_square(). First, need load FielDHub package typing, , can enter information describing design like :","code":"library(FielDHub) lsd <- latin_square( t = 5, reps = 2, plotNumber = 101, planter = \"serpentine\", seed = 1238 )"},{"path":"https://didiermurillof.github.io/FielDHub/articles/latin_square.html","id":"details-on-the-inputs-entered-in-latin_square-above","dir":"Articles","previous_headings":"2. Using the FielDHub function: latin_square()","what":"Details on the inputs entered in latin_square() above","title":"Latin Square Design","text":"t = 5 number treatments. reps = 2 number replications (squares). plotNumber = 101 starting plot number. planter = \"cartesian\" plot order layout. locationNames = \"FARGO\" optional name location. seed = 1238 random seed replicate identical randomizations.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/latin_square.html","id":"print-lsd-object","dir":"Articles","previous_headings":"2. Using the FielDHub function: latin_square()","what":"Print lsd object","title":"Latin Square Design","text":"","code":"print(lsd) Latin Square Design: Information on the design parameters: List of 4 $ treatments : int 5 $ squares : num 2 $ locationName: NULL $ seed : num 1238 10 First observations of the data frame with the latin_square field book: ID LOCATION PLOT SQUARE ROW COLUMN TREATMENT 1 1 1 101 1 Row 1 Column 1 T5 2 2 1 102 1 Row 1 Column 2 T1 3 3 1 103 1 Row 1 Column 3 T2 4 4 1 104 1 Row 1 Column 4 T4 5 5 1 105 1 Row 1 Column 5 T3 6 6 1 110 1 Row 2 Column 1 T4 7 7 1 109 1 Row 2 Column 2 T2 8 8 1 108 1 Row 2 Column 3 T3 9 9 1 107 1 Row 2 Column 4 T1 10 10 1 106 1 Row 2 Column 5 T5"},{"path":"https://didiermurillof.github.io/FielDHub/articles/latin_square.html","id":"access-to-lsd-object","dir":"Articles","previous_headings":"2. Using the FielDHub function: latin_square()","what":"Access to lsd object","title":"Latin Square Design","text":"latin_square() function returns list consisting information displayed output tabs FielDHub app: design information, plot layout, plot numbering, entries list, field book. accessible $ operator, .e.Β lsd$layoutRandom lsd$fieldBook. lsd$fieldBook list containing information every plot field, information location plot treatment plot. seen output , field book columns ID, LOCATION, PLOT, SQUARE, ROW, COLUMN, TREATMENT.","code":"field_book <- lsd$fieldBook head(lsd$fieldBook, 10) ID LOCATION PLOT SQUARE ROW COLUMN TREATMENT 1 1 1 101 1 Row 1 Column 1 T5 2 2 1 102 1 Row 1 Column 2 T1 3 3 1 103 1 Row 1 Column 3 T2 4 4 1 104 1 Row 1 Column 4 T4 5 5 1 105 1 Row 1 Column 5 T3 6 6 1 110 1 Row 2 Column 1 T4 7 7 1 109 1 Row 2 Column 2 T2 8 8 1 108 1 Row 2 Column 3 T3 9 9 1 107 1 Row 2 Column 4 T1 10 10 1 106 1 Row 2 Column 5 T5"},{"path":"https://didiermurillof.github.io/FielDHub/articles/latin_square.html","id":"plot-the-field-layout","dir":"Articles","previous_headings":"2. Using the FielDHub function: latin_square()","what":"Plot the field layout","title":"Latin Square Design","text":"plotting layout function coordinates ROW COLUMN, can use generic function plot() follow,","code":"plot(lsd)"},{"path":"https://didiermurillof.github.io/FielDHub/articles/methods.html","id":"print","dir":"Articles","previous_headings":"","what":"print()","title":"Methods in FielDHub","text":"print() function prints design parameters experiment first 10 rows field book. first 10 rows field book saved output function assigned variable.","code":"print(experiment) Randomized Complete Block Design (RCBD): Information on the design parameters: List of 7 $ blocks : num 3 $ number.of.treatments: num 12 $ treatments : chr [1:12] \"T1\" \"T2\" \"T3\" \"T4\" ... $ locations : num 2 $ plotNumber : num [1:6] 1001 1101 1201 2001 2101 ... $ locationNames : chr [1:2] \"A\" \"B\" $ seed : num 123 10 First observations of the data frame with the RCBD field book: ID LOCATION PLOT REP TREATMENT 1 1 A 1001 1 T3 2 2 A 1002 1 T12 3 3 A 1003 1 T10 4 4 A 1004 1 T2 5 5 A 1005 1 T6 6 6 A 1006 1 T11 7 7 A 1007 1 T5 8 8 A 1008 1 T4 9 9 A 1009 1 T9 10 10 A 1010 1 T8"},{"path":"https://didiermurillof.github.io/FielDHub/articles/methods.html","id":"summary","dir":"Articles","previous_headings":"","what":"summary()","title":"Methods in FielDHub","text":"summary() function outputs list design parameters layout randomization plot numbers.","code":"summary(experiment) Randomized Complete Block Design (RCBD): 1. Information on the design parameters: List of 8 $ blocks : num 3 $ number.of.treatments: num 12 $ treatments : chr [1:12] \"T1\" \"T2\" \"T3\" \"T4\" ... $ locations : num 2 $ plotNumber : num [1:6] 1001 1101 1201 2001 2101 ... $ locationNames : chr [1:2] \"A\" \"B\" $ seed : num 123 $ id_design : num 2 2. Layout randomization for each location: $Loc_A Block --Treatments-- [1,] \"1\" \"T3 T12 T10 T2 T6 T11 T5 T4 T9 T8 T1 T7\" [2,] \"2\" \"T11 T5 T3 T9 T4 T1 T7 T12 T10 T2 T6 T8\" [3,] \"3\" \"T9 T3 T4 T1 T11 T7 T5 T10 T8 T2 T12 T6\" $Loc_B Block --Treatments-- [1,] \"1\" \"T9 T12 T10 T7 T3 T4 T5 T6 T8 T2 T1 T11\" [2,] \"2\" \"T5 T8 T2 T1 T9 T3 T11 T12 T7 T6 T4 T10\" [3,] \"3\" \"T12 T4 T6 T8 T10 T9 T1 T2 T7 T5 T3 T11\" 3. Plot numbers layout: $Loc_A [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [1,] 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 [2,] 1112 1111 1110 1109 1108 1107 1106 1105 1104 1103 1102 1101 [3,] 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 $Loc_B [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [1,] 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 [2,] 2112 2111 2110 2109 2108 2107 2106 2105 2104 2103 2102 2101 [3,] 2201 2202 2203 2204 2205 2206 2207 2208 2209 2210 2211 2212 4. Structure of the data frame with the RCBD field book: 'data.frame': 72 obs. of 5 variables: $ ID : int 1 2 3 4 5 6 7 8 9 10 ... $ LOCATION : Factor w/ 2 levels \"A\",\"B\": 1 1 1 1 1 1 1 1 1 1 ... $ PLOT : int 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 ... $ REP : int 1 1 1 1 1 1 1 1 1 1 ... $ TREATMENT: chr \"T3\" \"T12\" \"T10\" \"T2\" ..."},{"path":"https://didiermurillof.github.io/FielDHub/articles/methods.html","id":"plot","dir":"Articles","previous_headings":"","what":"plot()","title":"Methods in FielDHub","text":"plot() function plots field input design, displayed FielDHub. can also saved variable. function parameters location layout, applicable.","code":"plot(experiment, l = 2, layout = 2)"},{"path":"https://didiermurillof.github.io/FielDHub/articles/multi_location_prep.html","id":"overview","dir":"Articles","previous_headings":"","what":"Overview","title":"Optimized Multi-Location P-rep Design","text":"Partially replicated (p-rep) designs commonly employed early generation field trials. type design characterized replication portion entries, remaining entries appearing experiment. Commonly, part treatments reps due arbitrary decision research, also cases, due technical reasons. replication ratio typically 1:4 (Cullis 2006), means portion treatment repeated twice p = 25%. However, design can adapted meet specific needs adjusting values pp level replication. example, standard varieties (checks) may included higher levels replication test lines. FielDHub, optimized multi-location p-rep design employs principles incomplete block designs (IBD) determine distribution replicated non-replicated treatments across multiple locations.","code":""},{"path":[]},{"path":"https://didiermurillof.github.io/FielDHub/articles/multi_location_prep.html","id":"across-location","dir":"Articles","previous_headings":"Optimization","what":"Across Location","title":"Optimized Multi-Location P-rep Design","text":"function multi_location_prep() uses incomplete blocks design approach (Edmondson 2020) optimize allocation replicated un-replicated treatments across environments.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/multi_location_prep.html","id":"within-location","dir":"Articles","previous_headings":"Optimization","what":"Within Location","title":"Optimized Multi-Location P-rep Design","text":"partially replicated (p-rep) design location undergoes optimization process involves following procedure: Given matrix XX integers (p-rep design within location), want ensure distance two occurrences treatment least distance dd. specifically, want modify XX ensure treatments appear twice within distance less dd resulting matrix. goal optimization process find modified matrix satisfies constraint maximizing measure deviation original matrix XX. case, measure deviation pairwise Euclidean distance occurrences treatment. process done function swap_pairs() uses heuristic algorithm starts distance d=3d = 3 pairs occurrences treatment, increases distance 11 repeats process either algorithm longer converges maximum number iterations reached. algorithm works first identifying pairs occurrences treatment closer dd. pair, function selects random occurrence different integer least dd away, swaps two occurrences. process repeated swaps can made increase pairwise Euclidean distances occurrences treatment.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/multi_location_prep.html","id":"toy-example","dir":"Articles","previous_headings":"Optimization > Within Location","what":"Toy Example","title":"Optimized Multi-Location P-rep Design","text":"Consider p-rep design ten treatments replicated twice 40 . matrix (field layout) experiment 6 rows 10 columns. X=X = initial p-rep design, notice two instances treatment 5 positioned next . Additionally, treatments 7 9 also situated adjacent cells. suboptimal allocations lead issues inaccurate results analyzing data experiment due short distance replicated treatments likely spatial correlation . following table shows pairwise distances replicated treatments","code":"[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [1,] 21 40 17 25 26 3 11 31 36 6 [2,] 5 5 33 8 48 29 43 23 1 45 [3,] 41 27 38 39 7 28 14 22 24 4 [4,] 4 47 18 7 2 35 6 20 12 46 [5,] 3 15 9 34 49 50 2 10 42 8 [6,] 32 16 19 9 10 13 37 1 44 30 geno Pos1 Pos2 DIST rA cA rB cB 5 5 2 8 1.000000 2 1 2 2 7 7 22 27 1.414214 4 4 3 5 9 9 17 24 1.414214 5 3 6 4 2 2 28 41 2.236068 4 5 5 7 10 10 30 47 3.162278 6 5 5 8 1 1 48 50 4.123106 6 8 2 9 6 6 40 55 4.242641 4 7 1 10 3 3 5 31 6.403124 5 1 1 6 8 8 20 59 6.708204 2 4 5 10 4 4 4 57 9.055385 4 1 3 10"},{"path":"https://didiermurillof.github.io/FielDHub/articles/multi_location_prep.html","id":"swap-pairs","dir":"Articles","previous_headings":"Optimization > Within Location","what":"Swap pairs","title":"Optimized Multi-Location P-rep Design","text":"can improve efficiency design swapping treatments close next using function swap_pairs() FielDHub R package. new matrix optimized p-rep design , distances pairwise treatments , can see, minimum distance algorithm reached 5. means treatments appear twice within distance less 5 resulting prep design. considerable improvement first version p-rep design FielDHub function multi_location_prep() internally optimization process uses function swap_pairs() maximize distance replicated treatments.","code":"library(FielDHub) B <- swap_pairs(X, starting_dist = 3) print(B$optim_design) [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [1,] 8 35 6 2 33 44 3 4 37 6 [2,] 43 30 25 5 39 29 19 11 36 45 [3,] 40 13 38 10 20 28 15 41 10 17 [4,] 1 27 18 31 32 22 24 21 12 5 [5,] 23 47 3 34 49 50 16 46 14 48 [6,] 7 26 2 42 9 1 8 7 4 9 print(B$pairwise_distance) geno Pos1 Pos2 DIST rA cA rB cB 9 9 30 60 5.000000 6 5 6 10 10 10 21 51 5.000000 3 4 3 9 2 2 18 19 5.099020 6 3 1 4 4 4 43 54 5.099020 1 8 6 9 1 1 4 36 5.385165 4 1 6 6 3 3 17 37 5.656854 5 3 1 7 5 5 20 58 6.324555 2 4 4 10 6 6 13 55 7.000000 1 3 1 10 7 7 6 48 7.000000 6 1 6 8 8 8 1 42 7.810250 1 1 6 7"},{"path":"https://didiermurillof.github.io/FielDHub/articles/multi_location_prep.html","id":"use-case-multi-location-p-rep-design","dir":"Articles","previous_headings":"","what":"Use case (Multi-Location P-rep Design)","title":"Optimized Multi-Location P-rep Design","text":"Suppose plant breeding field trial 150 entries tested across five environments, seven replications entry allowed. Additionally, project includes three checks; replicated six times. can generate optimized multi-location partially replicated design using parameters. strategy guarantees treatments present environments different amounts replications. can generate design using FielDHub Shiny app FielDHub multi_location_prep() standalone function R.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/multi_location_prep.html","id":"running-the-shiny-app","dir":"Articles","previous_headings":"","what":"Running the Shiny App","title":"Optimized Multi-Location P-rep Design","text":"launch app need run either ","code":"FielDHub::run_app() library(FielDHub) run_app()"},{"path":"https://didiermurillof.github.io/FielDHub/articles/multi_location_prep.html","id":"using-the-fieldhub-shiny-app","dir":"Articles","previous_headings":"","what":"1. Using the FielDHub Shiny App","title":"Optimized Multi-Location P-rep Design","text":"app running, click tab Partially Replicated Design select Optimized Multi-Location p-rep dropdown. , follow following steps show generate optimized partially replicated design.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/multi_location_prep.html","id":"inputs","dir":"Articles","previous_headings":"","what":"Inputs","title":"Optimized Multi-Location P-rep Design","text":"Import entries’ list? Choose whether import list entry numbers names genotypes treatments. selection , means app going generate synthetic data entries names treatment/genotypes based user inputs. selection Yes, entries list must fulfill specific format must .csv file. file must columns ENTRY NAME. ENTRY column must unique entry integer number treatment/genotype. column NAME must unique name identifies treatment/genotype. ENTRY NAME must unique, duplicates allowed. following table, show example entries list format. Enter number entries Input # Entries box comma separated list. example 150 entries, enter 150 box sample experiment. Select whether experiment contain checks Include checks? option. example experiment , set Yes. select Yes option, two boxes appear, first Input # Checks set many checks include experiment. case 3. Next option Input # Check’s Reps, set number replications check respectively comma separated list. replicating 3 checks 6 times, enter 6,6,6 box. Enter number locations Input # Locations. run experiment 5 locations, set Input # Locations 5. Set total number replications entries locations # Copies Per Entry dropdown box. example experiment, set 7. Select serpentine cartesian Plot Order Layout. example use default serpentine layout. ensure randomizations consistent across sessions, can set random seed box labeled Random Seed. example, set 2456. (Optional) Enter starting plot number Starting Plot Number box. Since experiment multiple locations, must enter comma separated list numbers length number locations input valid. example, set 1,1001,2001,3001,4001. (Optional) Enter location names Input Location Name box. Since experiment six locations, must enter comma separated list strings names environments. example, set LOC1,LOC2,LOC3,LOC4,LOC5. entered information experiment left side panel, click Run! button run design. prompted select dimensions field list options dropdown middle screen box labeled Select dimensions field. case, select 12 x 19. Click Randomize! button randomize experiment set field dimensions see output plots. change dimensions , must re-randomize. change inputs left side panel running experiment initially, click Run Randomize buttons , re-run new inputs.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/multi_location_prep.html","id":"outputs","dir":"Articles","previous_headings":"","what":"Outputs","title":"Optimized Multi-Location P-rep Design","text":"run Optimized Multi-Location P-rep Design FielDHub set dimensions field, several ways display information contained field book. first tab, Get Random, shows option change dimensions field re-randomize, well genotype allocation matrix generated optimized p-rep design, displays replications genotype location, much like matrix generated sparse allocation.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/multi_location_prep.html","id":"randomized-field","dir":"Articles","previous_headings":"Outputs","what":"Randomized Field","title":"Optimized Multi-Location P-rep Design","text":"Randomized Field tab displays graphical representation randomization entries field specified dimensions. replicated entries green colored cells, cells appearing location. display includes numbered labels rows columns. can copy field table save directly Excel file Copy Excel buttons top.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/multi_location_prep.html","id":"plot-number-field","dir":"Articles","previous_headings":"Outputs","what":"Plot Number Field","title":"Optimized Multi-Location P-rep Design","text":"Plot Number Field tab, table display field plots numbered according Plot Order Layout specified, either serpentine cartesian. can see corresponding entries plot number field book. Like Randomized Field tab, can copy table save Excel file Copy Excel buttons.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/multi_location_prep.html","id":"field-book","dir":"Articles","previous_headings":"Outputs","what":"Field Book","title":"Optimized Multi-Location P-rep Design","text":"Field Book displays information experimental design table format. contains specific plot number row column address entry, well corresponding treatment/genotype plot. table searchable, can filter data relevant columns.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/multi_location_prep.html","id":"using-the-fieldhub-function-multi_location_prep-","dir":"Articles","previous_headings":"","what":"2. Using the FielDHub function: multi_location_prep().","title":"Optimized Multi-Location P-rep Design","text":"can run design function multi_location_prep() FielDHub package. First, need load FielDHub package typing, , can enter information describing design like :","code":"library(FielDHub) optim_multi_prep <- multi_location_prep( lines = 150, l = 5, copies_per_entry = 7, checks = 3, rep_checks = c(6,6,6), plotNumber = c(1,1001,2001,3001,4001), locationNames = c(\"LOC1\", \"LOC2\", \"LOC3\", \"LOC4\", \"LOC5\"), seed = 2456 )"},{"path":"https://didiermurillof.github.io/FielDHub/articles/multi_location_prep.html","id":"details-on-the-inputs-entered-in-multi_location_prep-above","dir":"Articles","previous_headings":"2. Using the FielDHub function: multi_location_prep().","what":"Details on the inputs entered in multi_location_prep() above","title":"Optimized Multi-Location P-rep Design","text":"description inputs used generate design, lines = 150 number entries field. l = 5 number locations. copies_per_entry = 7 number copies entry. checks = 3 (optional) number checks. rep_checks = c(6,6,6) (optional) number replications check, vector length number checks. locationNames = c(\"LOC1\", \"LOC2\", \"LOC3\", \"LOC4\", \"LOC5\") optional names locations. seed = 2456 random seed replicate identical randomizations.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/multi_location_prep.html","id":"print-optim_multi_prep-object","dir":"Articles","previous_headings":"2. Using the FielDHub function: multi_location_prep().","what":"Print optim_multi_prep object","title":"Optimized Multi-Location P-rep Design","text":"print summary information object optim_multi_prep, can use generic function print(). multi_location_prep() function returns objects partially_replicated() addition list_locs, allocation, size_locations. object list_locs list data frames. data frame three columns; ENTRY, NAME REPS information randomize environment. object allocation binary allocation matrix genotypes locations, size_locations data frame column location row indicating size location (number field plots). example, can display allocation object. Let us print first ten genotypes allocation. Let us add two new columns allocation table. can add number copies genotype; 7 . can also add average allocation genotype. treatment appear 1.4 times average. can manipulate optim_multi_prep object list R. can first display design parameters randomizations following code: outputs:","code":"print(head(optim_multi_prep$allocation, 10)) LOC1 LOC2 LOC3 LOC4 LOC5 1 2 1 1 1 2 2 1 2 1 1 2 3 2 1 1 1 2 4 1 1 2 1 2 5 1 1 2 2 1 6 1 2 1 1 2 7 2 1 2 1 1 8 1 2 2 1 1 9 1 1 2 1 2 10 2 2 1 1 1 print(optim_multi_prep) Multi-Location Partially Replicated Design Replications within location: LOCATION Replicated Unreplicated 1 LOC1 63 90 2 LOC2 63 90 3 LOC3 63 90 4 LOC4 63 90 5 LOC5 63 90 Information on the design parameters: List of 7 $ rows : num [1:5] 19 19 19 19 19 $ columns : num [1:5] 12 12 12 12 12 $ min_distance : num [1:5] 3 3 3 3 3 $ incidence_in_rows: num [1:5] 4 2 3 5 2 $ locations : num 5 $ planter : chr \"serpentine\" $ seed : num 2456 10 First observations of the data frame with the partially_replicated field book: ID EXPT LOCATION YEAR PLOT ROW COLUMN CHECKS ENTRY TREATMENT 1 1 PrepExpt LOC1 2024 1 1 1 76 76 G-76 2 2 PrepExpt LOC1 2024 2 1 2 0 111 G-111 3 3 PrepExpt LOC1 2024 3 1 3 129 129 G-129 4 4 PrepExpt LOC1 2024 4 1 4 45 45 G-45 5 5 PrepExpt LOC1 2024 5 1 5 0 133 G-133 6 6 PrepExpt LOC1 2024 6 1 6 0 49 G-49 7 7 PrepExpt LOC1 2024 7 1 7 123 123 G-123 8 8 PrepExpt LOC1 2024 8 1 8 0 57 G-57 9 9 PrepExpt LOC1 2024 9 1 9 54 54 G-54 10 10 PrepExpt LOC1 2024 10 1 10 125 125 G-125"},{"path":"https://didiermurillof.github.io/FielDHub/articles/multi_location_prep.html","id":"access-to-optim_multi_prep-output","dir":"Articles","previous_headings":"2. Using the FielDHub function: multi_location_prep().","what":"Access to optim_multi_prep output","title":"Optimized Multi-Location P-rep Design","text":"objects accessible $ operator, .e.Β optim_multi_prep$layoutRandom[[1]] LOC1, optim_multi_prep$fieldBook fieldBook locations. optim_multi_prep$fieldBook data frame containing information every plot field, information location plot treatment plot. seen output , field book columns ID, EXPT, LOCATION, YEAR, PLOT, ROW, COLUMN, CHECKS, ENTRY, TREATMENT. Let us see first 10 rows field book experiment.","code":"field_book <- optim_multi_prep$fieldBook head(field_book, 10) ID EXPT LOCATION YEAR PLOT ROW COLUMN CHECKS ENTRY TREATMENT 1 1 PrepExpt LOC1 2024 1 1 1 76 76 G-76 2 2 PrepExpt LOC1 2024 2 1 2 0 111 G-111 3 3 PrepExpt LOC1 2024 3 1 3 129 129 G-129 4 4 PrepExpt LOC1 2024 4 1 4 45 45 G-45 5 5 PrepExpt LOC1 2024 5 1 5 0 133 G-133 6 6 PrepExpt LOC1 2024 6 1 6 0 49 G-49 7 7 PrepExpt LOC1 2024 7 1 7 123 123 G-123 8 8 PrepExpt LOC1 2024 8 1 8 0 57 G-57 9 9 PrepExpt LOC1 2024 9 1 9 54 54 G-54 10 10 PrepExpt LOC1 2024 10 1 10 125 125 G-125"},{"path":"https://didiermurillof.github.io/FielDHub/articles/multi_location_prep.html","id":"plot-field-layout","dir":"Articles","previous_headings":"2. Using the FielDHub function: multi_location_prep().","what":"Plot field layout","title":"Optimized Multi-Location P-rep Design","text":"plotting layout function coordinates ROW COLUMN field book object can use generic function plot() follows. plots first location, indexable location using dollar sign operator well.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/multi_location_prep.html","id":"field-layout-for-location-1","dir":"Articles","previous_headings":"2. Using the FielDHub function: multi_location_prep(). > Plot field layout","what":"Field Layout for Location 1","title":"Optimized Multi-Location P-rep Design","text":"figure , green plots contain replicated entries, gray plots contain entries appear .","code":"plot(optim_multi_prep, l = 1)"},{"path":"https://didiermurillof.github.io/FielDHub/articles/multi_location_prep.html","id":"field-layout-for-location-5","dir":"Articles","previous_headings":"2. Using the FielDHub function: multi_location_prep(). > Plot field layout","what":"Field Layout for Location 5","title":"Optimized Multi-Location P-rep Design","text":"Also, example location five:","code":"plot(optim_multi_prep, l = 5)"},{"path":[]},{"path":"https://didiermurillof.github.io/FielDHub/articles/news/fieldhub-0-1-0.html","id":"fieldhub-0-1-0","dir":"Articles > News","previous_headings":"","what":"FielDHub 0.1.0","title":"","text":"Photo Karsten WΓΌrth Unsplash","code":""},{"path":[]},{"path":[]},{"path":"https://didiermurillof.github.io/FielDHub/articles/news/fieldhub-0-1-0.html","id":"didier-murillo","dir":"Articles > News","previous_headings":"FielDHub 0.1.0","what":"Didier Murillo","title":"","text":"delighted announce initial release FielDHub CRAN! FielDHub conceived make quick easy generate, randomize, plot complex standard experimental designs. initial release version 0.1.0 recognition FielDHub development one year already used researchers NDSU, CIAT, well teaching. Install FielDHub :","code":"install.packages(\"FielDHub\")"},{"path":"https://didiermurillof.github.io/FielDHub/articles/news/fieldhub-0-1-0.html","id":"usage","dir":"Articles > News","previous_headings":"FielDHub 0.1.0","what":"Usage","title":"","text":"Get started using two simple lines code:","code":"library(FielDHub) run_app()"},{"path":"https://didiermurillof.github.io/FielDHub/articles/news/fieldhub-0-1-0.html","id":"relevant-features","dir":"Articles > News","previous_headings":"FielDHub 0.1.0","what":"Relevant Features","title":"","text":"Unreplicated partially replicated designs commonly used plant breeding forestry lack free tools available researchers make randomization. FielDHub provides easy way complete designs using app standalone functions diagonal_arrangement(), optimized_arrangement() RCBD_augmented(). Partially replicated design can done using function partially_replicated(). app provides novel features make randomization along field layout map. FielDHub’s features generating synthetic data along randomization, well plotting field layouts make app suitable teach statistic courses experimental design.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/news/fieldhub-0-1-0.html","id":"acknowledgements","dir":"Articles > News","previous_headings":"FielDHub 0.1.0","what":"Acknowledgements","title":"","text":"FielDHub long time coming, wouldn’t possible without devoted community users, many gone contribute fixes new ideas. like particularly thank Dr.Β Richard Horsley (Professor, Department Head & Barley Breeder Department Plant Sciences) sponsored development project. Also, big thanks go Dr.Β Ana MarΓ­a Heilman Dr.Β Andrew Green support plant breeding/biological background. project without contributions knowledge Dr.Β Salvador Gezan. came project critical moment ideas code, went beyond expected. Thank Johan Aparicio Thomas Walk contributions FielDHub. FielDHub submitted published Journal Open Source Software. peer review process done Thiago de Paula Oliveira (Reviewer), David LeBauer (Reviewer), Charlotte Soneson (Editor). Thank work, effort, contributions.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/news/fieldhub-1-2-0.html","id":"fieldhub-1-2-0","dir":"Articles > News","previous_headings":"","what":"FielDHub 1.2.0","title":"","text":"Photo Vackground Unsplash","code":""},{"path":[]},{"path":[]},{"path":"https://didiermurillof.github.io/FielDHub/articles/news/fieldhub-1-2-0.html","id":"didier-murillo","dir":"Articles > News","previous_headings":"FielDHub 1.2.0","what":"Didier Murillo","title":"","text":"happy announce release FielDHub v1.2.0. 12 months hard work commitment. new version comes many changes, new features, better graphical user interface design shiny app.","code":""},{"path":[]},{"path":"https://didiermurillof.github.io/FielDHub/articles/news/fieldhub-1-2-0.html","id":"shiny-app","dir":"Articles > News","previous_headings":"FielDHub 1.2.0","what":"Shiny App","title":"","text":"Added help menu option app connect directly documentation available GitHub repository. Added vignettes help documentation standard functions modules available designs app. Added capability making multiple randomizations across different locations unreplicated, partially replicated, lattice, RCBD, factorial, split-plot, split-split-plot, strip-plot, IBD, RCD designs. Added capability produce heatmap visualizations simulated data experimental designs. Added action buttons copy save field maps field book outputs Excel. Added factorization options aid users creation randomizations mapping layouts unreplicated partially replicated designs. Previous version required users * mathematical calculation priori. Added filters search boxes field book tables. Updated UI/UX design home page. Grouped single diagonal arrangement, multiple diagonal arrangement, optimized arrangement augmented RCB designs one single module. Added action run button experimental designs prevent reactivity issues application. Improved standardized user experience features readability access. Improved error logging messages. Added features inform end-users utilization correct input data file formats associated metadata/columns, checking duplicate values input files, well data type verification. Added additional field layout visualization/map options experimental designs. Previous version mapping options unreplicated p-rep designs. Added drop-menu display multiple layout mapping option shown entry number plot experimental designs. means, now can visualize randomization layout option locations input. Added option repeating whole entries/experiments unreplicated diagonal arrangement design multiple experiments (previously called decision blocks). Added check box feature Augmented RCB design allow creation nurseries option randomizing experimental entries . user decides leave option unchecked, checks randomized, experimental entries shown consecutive order. Added check box option RCB design allow continuous plot numbering independently rep block number. Previous version coded replication plot number (.e., 101 =rep1, 201=rep2, etc.). Fixed restriction RCBD mapping layout allow use 25 entries. PS: better designs number entries higher 25 (info go : FIELD PLOT DESIGN ).","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/news/fieldhub-1-2-0.html","id":"standalone-functions-in-fieldhub-package","dir":"Articles > News","previous_headings":"FielDHub 1.2.0","what":"Standalone Functions in FielDHub Package","title":"","text":"partially_replicated() now generates randomization across multiple locations/sites. diagonal_arrangement() now generates randomization across multiple locations/sites. optimized_arrangement() now generates randomization across multiple locations/sites. partially_replicated() now allows entries/treatments replicates. , required least unreplicated entries. Functions optimized_arrangement(), diagonal_arrangement() partially_replicated() now return feedback input dimensions nrows ncols incorrect. RCBD() now includes argument (continuous) manage way sets plotting number. RCBD_augmented() now allows customization field dimensions inputting number rows columns nrows ncols arguments. RCBD_augmented() now returns feedback input dimensions nrows ncols match data entered. RCBD_augmented() random = FALSE now allows randomizing checks/controls user wants. Fixed bug full_factorial() CRD factorial design prevented option including possible factorial combinations. Added method print() class fieldLayout. See print(). Added method plot() class FieldHub returns object class fieldLayout. See plot(). method plot() can plot field layout designs output. possible pass arguments location, layout order others. detail see plot(), print() summary() methods FielDHub. Fixed bug diagonal_arrangement() kindExpt = DBUDC. problem affected random distribution checks case unbalanced control plot numbers experiment. Fixed bug diagonal_arrangement() kindExpt = DBUDC. problem affected merging data user data randomization data users wanted replicated entries across experiments.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/news/fieldhub-1-2-0.html","id":"acknowledgements","dir":"Articles > News","previous_headings":"FielDHub 1.2.0","what":"Acknowledgements","title":"","text":"FielDHub v1.2.0 long time coming, wouldn’t possible without effort contribution Matthew Seefeldt. Thank Johan Aparicio bugs reported. Thank Ana MarΓ­a Heilman support leadership.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/news/fieldhub-1-3-1.html","id":"fieldhub-1-3-1","dir":"Articles > News","previous_headings":"","what":"FielDHub 1.3.1","title":"","text":"Photo Markus Spiske Pexels","code":""},{"path":[]},{"path":[]},{"path":"https://didiermurillof.github.io/FielDHub/articles/news/fieldhub-1-3-1.html","id":"didier-murillo","dir":"Articles > News","previous_headings":"FielDHub 1.3.1","what":"Didier Murillo","title":"","text":"thrilled announce release FielDHub v1.3.1, culmination dedicated effort hard work. updated version includes improvements new features, including sparse allocation, optimized multi-location p-rep, . excited share new capabilities users.","code":""},{"path":[]},{"path":"https://didiermurillof.github.io/FielDHub/articles/news/fieldhub-1-3-1.html","id":"new-features-in-the-shiny-app","dir":"Articles > News","previous_headings":"FielDHub 1.3.1","what":"New Features in the Shiny App","title":"","text":"Added module generate Sparse allocation. Added module generating Optimized Multi-Location Partially Replicated (p-rep). Added vignettes help documentation new modules; Sparse Allocations Optimized Multi-Location Partially Replicated (p-rep) Designs app.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/news/fieldhub-1-3-1.html","id":"enhancements","dir":"Articles > News","previous_headings":"FielDHub 1.3.1 > New Features in the Shiny App","what":"Enhancements:","title":"","text":"Renamed Partially Replicated module Single Multi-Location p-rep Improved usability field dimensions dropdown menu reordering options based absolute value difference number rows columns option. affects unreplicated partially replicated design modules.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/news/fieldhub-1-3-1.html","id":"fix-bugs","dir":"Articles > News","previous_headings":"FielDHub 1.3.1 > New Features in the Shiny App","what":"Fix bugs:","title":"","text":"Fixed issue: Upload data CRD module.","code":""},{"path":[]},{"path":"https://didiermurillof.github.io/FielDHub/articles/news/fieldhub-1-3-1.html","id":"standalone-functions","dir":"Articles > News","previous_headings":"FielDHub 1.3.1 > New Features in the FielDHub Package:","what":"Standalone Functions","title":"","text":"Created do_optim() function. function generates sparse p-rep allocation multiple locations. optimized allocation using incomplete blocks. Created sparse_allocation() function. new function uses function, do_optim(), generate sparse allocation, uses function diagonal_arrangement() create unreplicated designs across multiple locations. Created multi_location_prep() function. uses within optimization function do_optim() generate partially replicated (p-rep) allocation, uses function partially_replicated() create p-rep designs across multiple locations. Created pairs_distance() function. function calculates pairwise distances elements matrix appears twice . Created swap_pairs() function. swaps pairs matrix integers optimizes p-rep design. function modifies input matrix XX ensure distance two occurrences integer least distance dd, swapping one occurrences random occurrence different integer least dd away. function starts starting dist d=3d = 3 increases 11 algorithm longer converges max number iterations performed. Created search_matrix_values() function. looks values appear row matrix return row number, value, frequency. Added optimization process partially replicated (p-rep) designs. uses function swap_pairs(). Added vignettes help documentation new functions.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/news/fieldhub-1-3-1.html","id":"enhancements-1","dir":"Articles > News","previous_headings":"FielDHub 1.3.1 > New Features in the FielDHub Package:","what":"Enhancements:","title":"","text":"partially_replicated() accepts custom field dimensions location. example, nrows = c(23, 20, 20) ncols = c(20, 23, 23) field rows columns three environments. Code refactoring diagonal_arrangement() function. Code refactoring utility function pREP(). Avoid cyclic reps incomplete block designs number treatments square.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/news/fieldhub-1-3-1.html","id":"acknowledgements","dir":"Articles > News","previous_headings":"","what":"Acknowledgements","title":"","text":"FielDHub v1.3.1 results dedicated effort contribution group individuals made release possible. want extend sincere gratitude Mr.Β Jean-Marc Montpetit contributions developing swap_pairs() pairs_distance() functions. help enhanced optimization partially replicated (p-rep) design. Thank , Dr.Β Salvador Gezan, contributions fresh ideas. also thank Matthew Seefeldt helping write documentation Johan Aparicio ideas reporting bugs. Thanks, Ana MarΓ­a Heilman, support leadership throughout development process.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/optimized_arrangement.html","id":"overview","dir":"Articles","previous_headings":"","what":"Overview","title":"Un-replicated Optimized Arrangement Design","text":"One un-replicated design can use FielDHub optimized arrangement. Unlike diagonal design, optimized arrangement completely randomizes positions checks instead putting systematic diagonal pattern(Clarke Stefanova 2011). Randomization subject restrictions. restrictions seek optimize distribution control plots field ensure spread keeping minimum distance . FielDHub includes function run experimental designs, features include options set number entries number checks experiment. Users can also choose run experiment multiple locations.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/optimized_arrangement.html","id":"use-case","dir":"Articles","previous_headings":"","what":"Use case","title":"Un-replicated Optimized Arrangement Design","text":"early generation plant breeding project needs test 401 genotypes winter wheat. planned carry experiment field containing 29 rows 15 columns plots. project, 401 genotypes allocated one experiment tested three locations. addition, three checks randomly included across field fill 34 plots representing 7.8% total number experimental plots.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/optimized_arrangement.html","id":"running-the-shiny-app","dir":"Articles","previous_headings":"","what":"Running the Shiny App","title":"Un-replicated Optimized Arrangement Design","text":"launch app need run either, ","code":"FielDHub::run_app() library(FielDHub) run_app()"},{"path":"https://didiermurillof.github.io/FielDHub/articles/optimized_arrangement.html","id":"using-the-fieldhub-shiny-app","dir":"Articles","previous_headings":"","what":"1. Using the FielDHub Shiny App","title":"Un-replicated Optimized Arrangement Design","text":"app running, go un-replicated Designs > Optimized Arrangement , follow following steps show generate un-replicated optimized arrangement design.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/optimized_arrangement.html","id":"inputs","dir":"Articles","previous_headings":"","what":"Inputs","title":"Un-replicated Optimized Arrangement Design","text":"selection , means app going generate synthetic data entries names treatment/genotypes based user inputs. selection Yes, entries list must fulfill specific format must .csv file. file must columns ENTRY, NAME, REPS. ENTRY column must unique entry integer number treatment/genotype. column NAME must unique name identifies treatment/genotype. REPS column must integer entry replications checks entries. ENTRY NAME must unique, duplicates allowed. following table, show example entries list format. example entry list three checks nine treatments/genotypes. crucial allocate checks top part file. Enter number checks Input # Checks box, 3 case. Enter number replications checks comma separated list containing number check Input # Check’s Reps box. example experiment, enter 12,11,11. Enter number entries/treatments Input # Entries box, 401 case. Select serpentine cartesian Plot Order Layout. example set serpentine layout. Since want run experiment 3 locations, set Input # Locations 3. ensure randomizations consistent across sessions, can set random seed box labeled random seed. instance, set 130. Enter name experiment Input Experiment Name box. example, PYT_WHEAT_22. Enter starting plot number Starting Plot Number box. experiment multiple locations, must enter comma separated list numbers length number locations input valid. Since 3 locations experiment, enter 1001,2001,3001. Enter name site/location Input Location box. case run experiment three locations, name location must enter separate comma, example: FARGO, CASSELTON, MINOT. entered information experiment left side panel, click Run! button run design. prompted select dimensions field list options drop middle screen box labeled Select dimensions field. case, select 15 x 29. Click Randomize! button randomize experiment set field dimensions see output plots. change dimensions , must re-randomize. change inputs left side panel running experiment initially, click Run Randomize buttons , re-run new inputs.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/optimized_arrangement.html","id":"outputs","dir":"Articles","previous_headings":"","what":"Outputs","title":"Un-replicated Optimized Arrangement Design","text":"run un-replicated optimized arrangement design FielDHub set dimensions field, several ways display information contained field book. first tab, Get Random, shows option change dimensions field re-randomize, well reference guide experiment design.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/optimized_arrangement.html","id":"data-input","dir":"Articles","previous_headings":"Outputs","what":"Data Input","title":"Un-replicated Optimized Arrangement Design","text":"second tab, Data Input, can see entries randomization list, well table checks number times appear field. list entries, reps check included well.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/optimized_arrangement.html","id":"randomized-field","dir":"Articles","previous_headings":"Outputs","what":"Randomized Field","title":"Un-replicated Optimized Arrangement Design","text":"Randomized Field tab displays graphical representation randomization entries field specified dimensions. checks colored uniquely, showing number times distributed throughout field. display includes numbered labels rows columns. can copy field table save directly Excel file Copy Excel buttons top.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/optimized_arrangement.html","id":"plot-number-field","dir":"Articles","previous_headings":"Outputs","what":"Plot Number Field","title":"Un-replicated Optimized Arrangement Design","text":"Plot Number Field tab, table display field plots numbered according Plot Order Layout specified, either serpentine cartesian. can see corresponding entries plot number field book. Like Randomized Field tab, can copy table save Excel file Copy Excel buttons.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/optimized_arrangement.html","id":"field-book","dir":"Articles","previous_headings":"Outputs","what":"Field Book","title":"Un-replicated Optimized Arrangement Design","text":"Field Book displays information experimental design table format. contains specific plot number row column address entry, well corresponding treatment plot. table searchable, can filter data relevant columns.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/optimized_arrangement.html","id":"using-the-fieldhub-function-optimized_arrangement-","dir":"Articles","previous_headings":"","what":"2. Using the FielDHub function: optimized_arrangement().","title":"Un-replicated Optimized Arrangement Design","text":"can run design function optimized_arrangement() FielDHub package. First, need load FielDHub package typing, , can enter information describing design like :","code":"library(FielDHub) optim_expt <- optimized_arrangement( nrows = 29, ncols = 15, lines = 401, amountChecks = c(12,11,11), checks = 3, l = 3, plotNumber = c(1001,2001,3001), exptName = \"WINTER_WHEAT_22\", locationNames = c(\"FARGO\", \"CASSELTON\", \"MINOT\"), seed = 130 )"},{"path":"https://didiermurillof.github.io/FielDHub/articles/optimized_arrangement.html","id":"details-on-the-inputs-entered-in-optimized_arrangement-above","dir":"Articles","previous_headings":"2. Using the FielDHub function: optimized_arrangement().","what":"Details on the inputs entered in optimized_arrangement() above","title":"Un-replicated Optimized Arrangement Design","text":"description inputs used generate design, nrows = 29 number rows field. ncols = 15 number columns field. lines = 401 number entries amountChecks = c(12,11,11) values representing respective replicates check, integer total number checks. checks = 3 number checks. l = 3 number locations. plotNumber = c(1001,2001,3001) starting plot number location respectively, single number 1 location. exptName = \"WINTER_WHEAT_22\" optional name experiment. locationNames = c(\"FARGO\", \"CASSELTON\", \"MINOT\") values representing respective name location. seed = 130 random seed replicate identical randomizations.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/optimized_arrangement.html","id":"print-optim_expt-object","dir":"Articles","previous_headings":"2. Using the FielDHub function: optimized_arrangement().","what":"Print optim_expt object","title":"Un-replicated Optimized Arrangement Design","text":"print summary information object optim_expt, can use generic function print().","code":"print(optim_expt) Un-replicated Optimized Arrangement Design Information on the design parameters: List of 10 $ rows : num 29 $ columns : num 15 $ min_distance: num [1:3] 2 3.16 2.24 $ treatments : num 401 $ checks : int 3 $ entry_checks: int [1:3] 1 2 3 $ rep_checks : num [1:3] 12 11 11 $ locations : num 3 $ planter : chr \"serpentine\" $ seed : num 130 10 First observations of the data frame with the optimized_arrangement field book: ID EXPT LOCATION YEAR PLOT ROW COLUMN CHECKS ENTRY TREATMENT 1 1 WINTER_WHEAT_22 FARGO 2024 1001 1 1 0 70 G70 2 2 WINTER_WHEAT_22 FARGO 2024 1002 1 2 0 357 G357 3 3 WINTER_WHEAT_22 FARGO 2024 1003 1 3 0 217 G217 4 4 WINTER_WHEAT_22 FARGO 2024 1004 1 4 0 280 G280 5 5 WINTER_WHEAT_22 FARGO 2024 1005 1 5 0 259 G259 6 6 WINTER_WHEAT_22 FARGO 2024 1006 1 6 0 50 G50 7 7 WINTER_WHEAT_22 FARGO 2024 1007 1 7 0 223 G223 8 8 WINTER_WHEAT_22 FARGO 2024 1008 1 8 0 348 G348 9 9 WINTER_WHEAT_22 FARGO 2024 1009 1 9 0 180 G180 10 10 WINTER_WHEAT_22 FARGO 2024 1010 1 10 0 153 G153"},{"path":"https://didiermurillof.github.io/FielDHub/articles/optimized_arrangement.html","id":"access-to-optim_expt-object","dir":"Articles","previous_headings":"2. Using the FielDHub function: optimized_arrangement().","what":"Access to optim_expt object","title":"Un-replicated Optimized Arrangement Design","text":"optimized_arrangement() function returns list consisting information displayed output tabs FielDHub app: design information, plot layout, plot numbering, entries list, field book. Accessible $ operator, .e.Β optim_expt$layoutRandom optim_expt$fieldBook. optim_expt$fieldBook data frame containing information every plot field, information location plot treatment plot. seen output , field book columns ID, EXPT, LOCATION, YEAR, PLOT, ROW, COLUMN, CHECKS, ENTRY, TREATMENT. Let us see first 10 rows field book experiment.","code":"field_book <- optim_expt$fieldBook head(field_book, 10) ID EXPT LOCATION YEAR PLOT ROW COLUMN CHECKS ENTRY TREATMENT 1 1 WINTER_WHEAT_22 FARGO 2024 1001 1 1 0 70 G70 2 2 WINTER_WHEAT_22 FARGO 2024 1002 1 2 0 357 G357 3 3 WINTER_WHEAT_22 FARGO 2024 1003 1 3 0 217 G217 4 4 WINTER_WHEAT_22 FARGO 2024 1004 1 4 0 280 G280 5 5 WINTER_WHEAT_22 FARGO 2024 1005 1 5 0 259 G259 6 6 WINTER_WHEAT_22 FARGO 2024 1006 1 6 0 50 G50 7 7 WINTER_WHEAT_22 FARGO 2024 1007 1 7 0 223 G223 8 8 WINTER_WHEAT_22 FARGO 2024 1008 1 8 0 348 G348 9 9 WINTER_WHEAT_22 FARGO 2024 1009 1 9 0 180 G180 10 10 WINTER_WHEAT_22 FARGO 2024 1010 1 10 0 153 G153"},{"path":"https://didiermurillof.github.io/FielDHub/articles/optimized_arrangement.html","id":"plot-the-field-layout","dir":"Articles","previous_headings":"2. Using the FielDHub function: optimized_arrangement().","what":"Plot the field layout","title":"Un-replicated Optimized Arrangement Design","text":"plotting layout function coordinates ROW COLUMN field book object can use generic function plot() follow, figure shows map experiment randomized un-replicated optimized arrangement design. Gray plots represent un-replicated treatments, distinctively colored check plots randomly replicated throughout field. possible pass arguments plot() specific location. example, can plot specifically layout location 2.","code":"plot(optim_expt) plot(optim_expt, l = 2)"},{"path":[]},{"path":"https://didiermurillof.github.io/FielDHub/articles/partially_replicated.html","id":"overview","dir":"Articles","previous_headings":"","what":"Overview","title":"Partially Replicated (p-rep) Design","text":"Partially replicated designs commonly employed early generation field trials. type design characterized replication portion entries, remaining entries appearing experiment. Commonly, part treatments reps due arbitrary decision research, also cases, due technical reasons. replication ratio typically 1:4 (Cullis 2006), means portion treatment repeated twice p = 25%. However, design can adapted meet specific needs adjusting values pp level replication. example, standard varieties (checks) may included higher levels replication test lines. FielDHub, users can set number entries reps, well number entries appear . can also choose run experiment multiple locations.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/partially_replicated.html","id":"optimization","dir":"Articles","previous_headings":"","what":"Optimization","title":"Partially Replicated (p-rep) Design","text":"partially replicated (p-rep) design location undergoes optimization process involves following procedure: Given matrix XX integers (p-rep design within location), want ensure distance two occurrences treatment least distance dd. specifically, want modify XX ensure treatments appear twice within distance less dd resulting matrix. goal optimization process find modified matrix satisfies constraint maximizing measure deviation original matrix XX. case, measure deviation pairwise Euclidean distance occurrences treatment. process done function swap_pairs() uses heuristic algorithm starts distance d=3d = 3 pairs occurrences treatment, increases distance 11 repeats process either algorithm longer converges maximum number iterations reached. algorithm works first identifying pairs occurrences treatment closer dd. pair, function selects random occurrence different integer least dd away, swaps two occurrences. process repeated swaps can made increase pairwise Euclidean distances occurrences treatment.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/partially_replicated.html","id":"toy-example","dir":"Articles","previous_headings":"Optimization","what":"Toy Example","title":"Partially Replicated (p-rep) Design","text":"Consider p-rep design ten treatments replicated twice forty . matrix (field layout) experiment 6 rows 10 columns. X=X = initial p-rep design, notice two instances treatment 5 positioned next . Additionally, treatments 7 9 also situated adjacent cells. suboptimal allocations lead issues inaccurate results analyzing data experiment due short distance replicated treatments likely spatial correlation . following table shows pairwise distances replicated treatments","code":"[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [1,] 21 40 17 25 26 3 11 31 36 6 [2,] 5 5 33 8 48 29 43 23 1 45 [3,] 41 27 38 39 7 28 14 22 24 4 [4,] 4 47 18 7 2 35 6 20 12 46 [5,] 3 15 9 34 49 50 2 10 42 8 [6,] 32 16 19 9 10 13 37 1 44 30 geno Pos1 Pos2 DIST rA cA rB cB 5 5 2 8 1.000000 2 1 2 2 7 7 22 27 1.414214 4 4 3 5 9 9 17 24 1.414214 5 3 6 4 2 2 28 41 2.236068 4 5 5 7 10 10 30 47 3.162278 6 5 5 8 1 1 48 50 4.123106 6 8 2 9 6 6 40 55 4.242641 4 7 1 10 3 3 5 31 6.403124 5 1 1 6 8 8 20 59 6.708204 2 4 5 10 4 4 4 57 9.055385 4 1 3 10"},{"path":"https://didiermurillof.github.io/FielDHub/articles/partially_replicated.html","id":"swap-pairs","dir":"Articles","previous_headings":"Optimization","what":"Swap pairs","title":"Partially Replicated (p-rep) Design","text":"can improve efficiency design swapping treatments close next using function swap_pairs() FielDHub R package. new matrix optimized p-rep design , distances pairwise treatments , can see, minimum distance algorithm reached 5. means treatments appear twice within distance less 5 resulting prep design. considerable improvement first version p-rep design FielDHub function partially_replicated() internally optimization process uses function swap_pairs() maximize distance replicated treatments.","code":"library(FielDHub) B <- swap_pairs(X, starting_dist = 3) print(B$optim_design) [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [1,] 8 35 6 2 33 44 3 4 37 6 [2,] 43 30 25 5 39 29 19 11 36 45 [3,] 40 13 38 10 20 28 15 41 10 17 [4,] 1 27 18 31 32 22 24 21 12 5 [5,] 23 47 3 34 49 50 16 46 14 48 [6,] 7 26 2 42 9 1 8 7 4 9 print(B$pairwise_distance) geno Pos1 Pos2 DIST rA cA rB cB 9 9 30 60 5.000000 6 5 6 10 10 10 21 51 5.000000 3 4 3 9 2 2 18 19 5.099020 6 3 1 4 4 4 43 54 5.099020 1 8 6 9 1 1 4 36 5.385165 4 1 6 6 3 3 17 37 5.656854 5 3 1 7 5 5 20 58 6.324555 2 4 4 10 6 6 13 55 7.000000 1 3 1 10 7 7 6 48 7.000000 6 1 6 8 8 8 1 42 7.810250 1 1 6 7"},{"path":"https://didiermurillof.github.io/FielDHub/articles/partially_replicated.html","id":"acknowledge","dir":"Articles","previous_headings":"Optimization","what":"Acknowledge","title":"Partially Replicated (p-rep) Design","text":"like acknowledge Mr.Β Jean-Marc Montpetit contributing code ideas swap_pairs() pairs_distance() functions. contributions significant impact improving partially replicated (p-rep) design R package FielDHub. thank valuable contributions.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/partially_replicated.html","id":"use-case","dir":"Articles","previous_headings":"","what":"Use case","title":"Partially Replicated (p-rep) Design","text":"Consider plant breeding field trial 300 plots containing 75 entries appearing two times , 150 entries appearing . field trial arranged field 15 rows 20 columns. case, breeder decided replicate genotypes share significant generic information (75), well leave just one copy genotypes siblings (150).","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/partially_replicated.html","id":"running-the-shiny-app","dir":"Articles","previous_headings":"","what":"Running the Shiny App","title":"Partially Replicated (p-rep) Design","text":"launch app need run either ","code":"FielDHub::run_app() library(FielDHub) run_app()"},{"path":"https://didiermurillof.github.io/FielDHub/articles/partially_replicated.html","id":"using-the-fieldhub-shiny-app","dir":"Articles","previous_headings":"","what":"1. Using the FielDHub Shiny App","title":"Partially Replicated (p-rep) Design","text":"app running, click tab Partially Replicated Design select Single Multi-Location p-rep dropdown. , follow following steps show generate partially replicated design.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/partially_replicated.html","id":"inputs","dir":"Articles","previous_headings":"","what":"Inputs","title":"Partially Replicated (p-rep) Design","text":"selection , means app going generate synthetic data entries names treatment/genotypes based user inputs. selection Yes, entries list must fulfill specific format must .csv file. file must columns ENTRY, NAME, REPS. ENTRY column must unique entry integer number treatment/genotype. column NAME must unique name identifies treatment/genotype. REPS column must integer number replications groups. ENTRY NAME must unique, duplicates allowed. following table, show example entries list format. example entry list four treatments/genotypes appear twice 8 appear just . Enter number entries per replicate group # Entries per Rep Group box comma separated list. example 2 groups 85 130 entries. , enter 75, 150 box sample experiment. Enter number replications per group # Rep per Group box. example 2 1 replications 2 groups, enter 2, 1 box. Enter number locations Input # Locations. run experiment single location, set Input # Locations 1. Select serpentine cartesian Plot Order Layout. example use default serpentine layout. ensure randomizations consistent across sessions, can set random seed box labeled random seed. example, set 1245. Enter starting plot number Starting Plot Number box. experiment multiple locations, must enter comma separated list numbers length number locations input valid. entered information experiment left side panel, click Run! button run design. prompted select dimensions field list options drop middle screen box labeled Select dimensions field. case, select 15 x 20. Click Randomize! button randomize experiment set field dimensions see output plots. change dimensions , must re-randomize. change inputs left side panel running experiment initially, click Run Randomize buttons , re-run new inputs.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/partially_replicated.html","id":"outputs","dir":"Articles","previous_headings":"","what":"Outputs","title":"Partially Replicated (p-rep) Design","text":"run single diagonal arrangement FielDHub set dimensions field, several ways display information contained field book. first tab, Get Random, shows option change dimensions field re-randomize, well reference guide experiment design.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/partially_replicated.html","id":"data-input","dir":"Articles","previous_headings":"Outputs","what":"Data Input","title":"Partially Replicated (p-rep) Design","text":"second tab, Data Input, can see entries randomization list, well table checks number times appear field. list entries, reps check included well.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/partially_replicated.html","id":"randomized-field","dir":"Articles","previous_headings":"Outputs","what":"Randomized Field","title":"Partially Replicated (p-rep) Design","text":"Randomized Field tab displays graphical representation randomization entries field specified dimensions. checks green colored cells, display includes numbered labels rows columns. can copy field table save directly Excel file Copy Excel buttons top.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/partially_replicated.html","id":"plot-number-field","dir":"Articles","previous_headings":"Outputs","what":"Plot Number Field","title":"Partially Replicated (p-rep) Design","text":"Plot Number Field tab, table display field plots numbered according Plot Order Layout specified, either serpentine cartesian. can see corresponding entries plot number field book. Like Randomized Field tab, can copy table save Excel file Copy Excel buttons.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/partially_replicated.html","id":"field-book","dir":"Articles","previous_headings":"Outputs","what":"Field Book","title":"Partially Replicated (p-rep) Design","text":"Field Book displays information experimental design table format. contains specific plot number row column address entry, well corresponding treatment plot. table searchable, can filter data relevant columns.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/partially_replicated.html","id":"using-the-fieldhub-function-partially_replicated-","dir":"Articles","previous_headings":"","what":"2. Using the FielDHub function: partially_replicated().","title":"Partially Replicated (p-rep) Design","text":"can run design function partially_replicated() FielDHub package. First, need load FielDHub package typing, , can enter information describing design like :","code":"library(FielDHub) prep <- partially_replicated( nrows = 15, ncols = 20, repGens = c(75,150), repUnits = c(2,1), planter = \"serpentine\", plotNumber = 101, l = 1, exptName = \"Expt1\", locationNames = \"PALMIRA\", seed = 1245, )"},{"path":"https://didiermurillof.github.io/FielDHub/articles/partially_replicated.html","id":"details-on-the-inputs-entered-in-optimized_arrangement-above","dir":"Articles","previous_headings":"2. Using the FielDHub function: partially_replicated().","what":"Details on the inputs entered in optimized_arrangement() above","title":"Partially Replicated (p-rep) Design","text":"description inputs used generate design, nrows = 15 number rows field. ncols = 20 number columns field. repGens = c(75,150) values groups replicate repUnits = c(2,1) values representing respective replicates group. planter = \"serpentine\" layout order. plotNumber = 101 starting plot number experiment. l = 1 number locations. exptName = \"Expt1\" optional name experiment. locationNames = \"PALMIRA\" optional name locations. seed = 1245 random seed replicate identical randomizations.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/partially_replicated.html","id":"print-prep-object","dir":"Articles","previous_headings":"2. Using the FielDHub function: partially_replicated().","what":"Print prep object","title":"Partially Replicated (p-rep) Design","text":"print summary information object prep, can use generic function print().","code":"print(prep) Partially Replicated Design Replications within location: LOCATION Replicated Unreplicated 1 PALMIRA 75 150 Information on the design parameters: List of 7 $ rows : num 15 $ columns : num 20 $ min_distance : num 7 $ incidence_in_rows: num 3 $ locations : num 1 $ planter : chr \"serpentine\" $ seed : num 1245 10 First observations of the data frame with the partially_replicated field book: ID EXPT LOCATION YEAR PLOT ROW COLUMN CHECKS ENTRY TREATMENT 1 1 Expt1 PALMIRA 2024 101 1 1 44 44 G44 2 2 Expt1 PALMIRA 2024 102 1 2 0 102 G102 3 3 Expt1 PALMIRA 2024 103 1 3 71 71 G71 4 4 Expt1 PALMIRA 2024 104 1 4 0 107 G107 5 5 Expt1 PALMIRA 2024 105 1 5 8 8 G8 6 6 Expt1 PALMIRA 2024 106 1 6 13 13 G13 7 7 Expt1 PALMIRA 2024 107 1 7 0 170 G170 8 8 Expt1 PALMIRA 2024 108 1 8 67 67 G67 9 9 Expt1 PALMIRA 2024 109 1 9 0 123 G123 10 10 Expt1 PALMIRA 2024 110 1 10 0 105 G105"},{"path":"https://didiermurillof.github.io/FielDHub/articles/partially_replicated.html","id":"access-to-prep-output","dir":"Articles","previous_headings":"2. Using the FielDHub function: partially_replicated().","what":"Access to prep output","title":"Partially Replicated (p-rep) Design","text":"partially_replicated() function returns list consisting information displayed output tabs FielDHub app: design information, plot layout, plot numbering, entries list, field book. Accessible $ operator, .e.Β prep$layoutRandom prep$fieldBook. prep$fieldBook list containing information every plot field, information location plot treatment plot. seen output , field book columns ID, EXPT, LOCATION, YEAR, PLOT, ROW, COLUMN, CHECKS, ENTRY, TREATMENT. Let us see first 10 rows field book experiment.","code":"field_book <- prep$fieldBook head(field_book, 10) ID EXPT LOCATION YEAR PLOT ROW COLUMN CHECKS ENTRY TREATMENT 1 1 Expt1 PALMIRA 2024 101 1 1 44 44 G44 2 2 Expt1 PALMIRA 2024 102 1 2 0 102 G102 3 3 Expt1 PALMIRA 2024 103 1 3 71 71 G71 4 4 Expt1 PALMIRA 2024 104 1 4 0 107 G107 5 5 Expt1 PALMIRA 2024 105 1 5 8 8 G8 6 6 Expt1 PALMIRA 2024 106 1 6 13 13 G13 7 7 Expt1 PALMIRA 2024 107 1 7 0 170 G170 8 8 Expt1 PALMIRA 2024 108 1 8 67 67 G67 9 9 Expt1 PALMIRA 2024 109 1 9 0 123 G123 10 10 Expt1 PALMIRA 2024 110 1 10 0 105 G105"},{"path":"https://didiermurillof.github.io/FielDHub/articles/partially_replicated.html","id":"plot-field-layout","dir":"Articles","previous_headings":"2. Using the FielDHub function: partially_replicated().","what":"Plot field layout","title":"Partially Replicated (p-rep) Design","text":"plotting layout function coordinates ROW COLUMN field book object can use generic function plot() follow, figure , green plots contain replicated entries, gray plots contain entries appear .","code":"plot(prep)"},{"path":[]},{"path":"https://didiermurillof.github.io/FielDHub/articles/rcbd.html","id":"using-the-fieldhub-shiny-app","dir":"Articles","previous_headings":"","what":"1. Using the FielDHub Shiny App","title":"Randomized Complete Block Design","text":"launch app need run either app running, go Designs > Randomized Complete Block Designs (RCBD) , follow following steps show generate kind design example 24 treatments 4 reps. run experiment just one location.","code":"FielDHub::run_app() library(FielDHub) run_app()"},{"path":"https://didiermurillof.github.io/FielDHub/articles/rcbd.html","id":"inputs","dir":"Articles","previous_headings":"","what":"Inputs","title":"Randomized Complete Block Design","text":"selection , means app going generate synthetic data entries names treatment/genotypes based user inputs. selection Yes, entries list must fulfill specific format must .csv file. file must single column TREATMENT, containing list unique names identify treatment/genotype. Duplicate values allowed, entries must unique. following, show example entries list format. example entry list 10 treatments. Input number treatments Input # Treatments box. Set 24. Select number replications treatments Input # Full Reps box. number treatments number full reps set dimensions field. Set 4. Enter number locations Input # Locations. run experiment single location, set 1. Select serpentine cartesian Plot Order Layout. example use default serpentine layout. Enter starting plot number Starting Plot Number box. experiment multiple locations, must enter comma separated list numbers length number locations input valid. case, set 101. Enter name location experiment Input Location box. multiple locations, name must comma separated list. Set \"FARGO\". ensure randomizations consistent across sessions, can set random seed box labeled random seed. example, set 1237. entered information experiment left side panel, click Run! button run design.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/rcbd.html","id":"outputs","dir":"Articles","previous_headings":"","what":"Outputs","title":"Randomized Complete Block Design","text":"run randomized complete block design FielDHub, several ways display information contained field book.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/rcbd.html","id":"field-layout","dir":"Articles","previous_headings":"Outputs","what":"Field Layout","title":"Randomized Complete Block Design","text":"first click run button randomized complete block design, FielDHub displays Field Layout tab, shows entries arrangement field. box display, can change layout field change location displayed. can also display heatmap field changing Type Plot Heatmap. view heatmap, must first simulate experiment described field Simulate! button. pop-window appear can enter variable want simulate along minimum maximum values.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/rcbd.html","id":"field-book","dir":"Articles","previous_headings":"Outputs","what":"Field Book","title":"Randomized Complete Block Design","text":"Field Book displays information experimental design table format. contains specific plot number row column address entry, well corresponding treatment plot. table searchable, can filter data relevant columns. simulated data heatmap, additional column variable appears field book.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/rcbd.html","id":"using-the-fieldhub-function-rcbd","dir":"Articles","previous_headings":"","what":"2. Using the FielDHub function: RCBD()","title":"Randomized Complete Block Design","text":"can run design function FielDHub package, RCBD(). First, need load FielDHub package typing, , can enter information describing design like :","code":"library(FielDHub) rcbd <- RCBD( t = 24, reps = 4, l = 1, plotNumber = 101, locationNames = \"FARGO\", seed = 1237 )"},{"path":"https://didiermurillof.github.io/FielDHub/articles/rcbd.html","id":"details-on-the-inputs-entered-in-rcbd-above","dir":"Articles","previous_headings":"2. Using the FielDHub function: RCBD()","what":"Details on the inputs entered in RCBD() above","title":"Randomized Complete Block Design","text":"description inputs used generate design, t = 24 number treatments. reps = 4 number replications treatment. l = 1 number locations. plotNumber = 101 starting plot number. locationNames = \"FARGO\" optional name location. seed = 1234 random seed replicate identical randomizations.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/rcbd.html","id":"print-rcbd-object","dir":"Articles","previous_headings":"2. Using the FielDHub function: RCBD()","what":"Print rcbd object","title":"Randomized Complete Block Design","text":"","code":"print(rcbd) Randomized Complete Block Design (RCBD): Information on the design parameters: List of 7 $ blocks : num 4 $ number.of.treatments: num 24 $ treatments : chr [1:24] \"T1\" \"T2\" \"T3\" \"T4\" ... $ locations : num 1 $ plotNumber : num [1:4] 101 201 301 401 $ locationNames : chr \"FARGO\" $ seed : num 1237 10 First observations of the data frame with the RCBD field book: ID LOCATION PLOT REP TREATMENT 1 1 FARGO 101 1 T6 2 2 FARGO 102 1 T1 3 3 FARGO 103 1 T21 4 4 FARGO 104 1 T7 5 5 FARGO 105 1 T14 6 6 FARGO 106 1 T17 7 7 FARGO 107 1 T12 8 8 FARGO 108 1 T11 9 9 FARGO 109 1 T3 10 10 FARGO 110 1 T5"},{"path":"https://didiermurillof.github.io/FielDHub/articles/rcbd.html","id":"access-to-rcbd-object","dir":"Articles","previous_headings":"2. Using the FielDHub function: RCBD()","what":"Access to RCBD object","title":"Randomized Complete Block Design","text":"function RCBD returns list consisting information displayed output tabs FielDHub app: design information, plot layout, plot numbering, entries list, field book. accessible $ operator, .e.Β rcbd$layoutRandom rcbd$fieldBook. rcbd$fieldBook list containing information every plot field, information location plot treatment plot. seen output , field book columns ID, LOCATION, PLOT, REP, IBLOCK, UNIT, ENTRY, TREATMENT.","code":"field_book <- rcbd$fieldBook head(rcbd$fieldBook, 10) ID LOCATION PLOT REP TREATMENT 1 1 FARGO 101 1 T6 2 2 FARGO 102 1 T1 3 3 FARGO 103 1 T21 4 4 FARGO 104 1 T7 5 5 FARGO 105 1 T14 6 6 FARGO 106 1 T17 7 7 FARGO 107 1 T12 8 8 FARGO 108 1 T11 9 9 FARGO 109 1 T3 10 10 FARGO 110 1 T5"},{"path":"https://didiermurillof.github.io/FielDHub/articles/rcbd.html","id":"plot-the-field-layout","dir":"Articles","previous_headings":"2. Using the FielDHub function: RCBD()","what":"Plot the field layout","title":"Randomized Complete Block Design","text":"plotting layout function coordinates ROW COLUMN, can use generic function plot() follows,","code":"plot(rcbd)"},{"path":"https://didiermurillof.github.io/FielDHub/articles/rectangular_lattice.html","id":"using-the-fieldhub-shiny-app","dir":"Articles","previous_headings":"","what":"1. Using the FielDHub Shiny App","title":"Rectangular Lattice Design","text":"generate rectangular lattice design using FielDHub app: First, go Lattice Designs > Rectangular Lattice , follow following steps show generate rectangular lattice design 56 treatments 3 reps.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/rectangular_lattice.html","id":"inputs","dir":"Articles","previous_headings":"","what":"Inputs","title":"Rectangular Lattice Design","text":"selection , means app going generate synthetic data entries names treatment based user inputs. selection Yes, entries list must fulfill specific format must .csv file. file must columns ENTRY NAME. ENTRY column must unique entry integer number treatment. column NAME must unique name identifies treatment. ENTRY NAME must unique, duplicates allowed. following table, show example entries list format. example entry list 12 treatments. Input number treatments Input # Treatments box. rectangular lattice design, number treatments must rectangular number, product two consecutive integers. example, 7 x 8 = 56 valid entry, use example. Select number replications treatments Input # Full Reps box, 3. Set number plots incomplete block Input # Plots per IBlock box, 7. Enter number locations Input # Locations. run experiment single location, set 1. Select serpentine cartesian Plot Order Layout. example use default cartesian layout. Enter starting plot number Starting Plot Number box. experiment multiple locations, must enter comma separated list numbers length number locations input valid. Set 101. Enter name location experiment Input Location box. multiple locations, name must comma separated list. Set \"FARGO\". ensure randomizations consistent across sessions, can set random seed box labeled random seed. example, set 1235. entered information experiment left side panel, click Run! button run design.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/rectangular_lattice.html","id":"outputs","dir":"Articles","previous_headings":"","what":"Outputs","title":"Rectangular Lattice Design","text":"run rectangular lattice design FielDHub, several ways display information contained field book.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/rectangular_lattice.html","id":"field-layout","dir":"Articles","previous_headings":"Outputs","what":"Field Layout","title":"Rectangular Lattice Design","text":"first click run button rectangular lattice design, FielDHub displays Field Layout tab, shows entries arrangement field. box display, can change layout field change location displayed. can also display heatmap field changing Type Plot Heatmap. view heatmap, must first simulate experiment described field Simulate! button. pop-window appear can enter variable want simulate along minimum maximum values.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/rectangular_lattice.html","id":"field-book","dir":"Articles","previous_headings":"Outputs","what":"Field Book","title":"Rectangular Lattice Design","text":"Field Book displays information experimental design table format. contains specific plot number row column address entry, well corresponding treatment plot. table searchable, can filter data relevant columns. simulated data heatmap, additional column variable appears field book.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/rectangular_lattice.html","id":"using-the-fieldhub-function-rectangular_lattice","dir":"Articles","previous_headings":"","what":"2. Using the FielDHub function: rectangular_lattice()","title":"Rectangular Lattice Design","text":"can run design function FielDHub package, rectangular_lattice(). First, need load FielDHub package typing, , can enter information describing design like :","code":"library(FielDHub) rect <- rectangular_lattice( t = 56, r = 3, k = 7, l = 1, plotNumber = 101, locationNames = \"FARGO\", seed = 1235 )"},{"path":"https://didiermurillof.github.io/FielDHub/articles/rectangular_lattice.html","id":"details-on-the-inputs-entered-in-rectangular_lattice-above","dir":"Articles","previous_headings":"2. Using the FielDHub function: rectangular_lattice()","what":"Details on the inputs entered in rectangular_lattice() above","title":"Rectangular Lattice Design","text":"t = 56 number treatments. r=3 number replicates. k = 7 number plots per incomplete block. l = 1 number locations. plotNumber = 101 starting plot number. locationNames = \"FARGO\" optional name location seed = 1235 random seed replicate identical randomizations.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/rectangular_lattice.html","id":"print-rect-object","dir":"Articles","previous_headings":"2. Using the FielDHub function: rectangular_lattice()","what":"Print rect object","title":"Rectangular Lattice Design","text":"","code":"print(rect) Rectangular Lattice Design Efficiency of design: Level Blocks D-Efficiency A-Efficiency A-Bound 1 1 3 1.0000000 1.0000000 1.0000000 2 2 24 0.8549751 0.8358296 0.8358296 Information on the design parameters: List of 7 $ Reps : num 3 $ iBlocks : num 8 $ NumberTreatments: num 56 $ NumberLocations : num 1 $ Locations : chr \"FARGO\" $ seed : num 1235 $ lambda : num 0.327 10 First observations of the data frame with the rectangular_lattice field book: ID LOCATION PLOT REP IBLOCK UNIT ENTRY TREATMENT 1 1 FARGO 101 1 1 1 38 G-38 2 2 FARGO 102 1 1 2 43 G-43 3 3 FARGO 103 1 1 3 2 G-2 4 4 FARGO 104 1 1 4 5 G-5 5 5 FARGO 105 1 1 5 22 G-22 6 6 FARGO 106 1 1 6 18 G-18 7 7 FARGO 107 1 1 7 15 G-15 8 8 FARGO 108 1 2 1 7 G-7 9 9 FARGO 109 1 2 2 33 G-33 10 10 FARGO 110 1 2 3 56 G-56"},{"path":"https://didiermurillof.github.io/FielDHub/articles/rectangular_lattice.html","id":"access-to-rect-object","dir":"Articles","previous_headings":"2. Using the FielDHub function: rectangular_lattice()","what":"Access to rect object","title":"Rectangular Lattice Design","text":"function rectangular_lattice() returns list consisting information displayed output tabs FielDHub app: design information, plot layout, plot numbering, entries list, field book. accessible $ operator, .e.Β rect$layoutRandom rect$fieldBook. rect$fieldBook list containing information every plot field, information location plot treatment plot. seen output , field book columns ID, LOCATION, PLOT, REP, IBLOCK, UNIT, ENTRY, TREATMENT.","code":"field_book <- rect$fieldBook head(rect$fieldBook, 10) ID LOCATION PLOT REP IBLOCK UNIT ENTRY TREATMENT 1 1 FARGO 101 1 1 1 38 G-38 2 2 FARGO 102 1 1 2 43 G-43 3 3 FARGO 103 1 1 3 2 G-2 4 4 FARGO 104 1 1 4 5 G-5 5 5 FARGO 105 1 1 5 22 G-22 6 6 FARGO 106 1 1 6 18 G-18 7 7 FARGO 107 1 1 7 15 G-15 8 8 FARGO 108 1 2 1 7 G-7 9 9 FARGO 109 1 2 2 33 G-33 10 10 FARGO 110 1 2 3 56 G-56"},{"path":"https://didiermurillof.github.io/FielDHub/articles/rectangular_lattice.html","id":"plot-the-field-layout","dir":"Articles","previous_headings":"2. Using the FielDHub function: rectangular_lattice()","what":"Plot the field layout","title":"Rectangular Lattice Design","text":"plotting layout function coordinates ROW COLUMN, can use generic function plot() follows,","code":"plot(rect)"},{"path":"https://didiermurillof.github.io/FielDHub/articles/row_column.html","id":"resolvable-row-column-design-two-step-optimization","dir":"Articles","previous_headings":"","what":"Resolvable Row-Column Design (Two-Step Optimization)","title":"Row-Column Design","text":"randomly generates resolvable row-column design.design optimized rows columns blocking factors. randomization can done across multiple locations.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/row_column.html","id":"overview","dir":"Articles","previous_headings":"","what":"Overview","title":"Row-Column Design","text":"Row-Column design FielDHub built two stages. first step constructs blocking factor Columns using Incomplete Block Units incomplete block design sets number incomplete blocks number Columns design, dimension equal number Rows. design generated, Rows used Row blocking factor optimized -Efficiency, levels within original Columns fixed. optimize Rows maintaining current optimized Columns, use heuristic algorithm swaps random treatment positions within given Column (Block) also selected random. algorithm begins calculating -Efficiency initial design, performs swap iteration, recalculates -Efficiency resulting design, compares previous one decide whether keep discard new design. iterative process repeated, default, 1000 times.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/row_column.html","id":"using-the-fieldhub-shiny-app","dir":"Articles","previous_headings":"","what":"1. Using the FielDHub Shiny App","title":"Row-Column Design","text":"generate Row-Column Design using FielDHub app: First, go Designs > Resolvable Row-Column Design (RRCD) , follow following steps show generate Row-Column Design 45 treatments, 5 rows 3 reps.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/row_column.html","id":"inputs","dir":"Articles","previous_headings":"","what":"Inputs","title":"Row-Column Design","text":"selection , means app going generate synthetic data entries names treatment based user inputs. selection Yes, entries list must fulfill specific format must .csv file. file must columns ENTRY NAME. ENTRY column must unique entry integer number treatment. column NAME must unique name identifies treatment. ENTRY NAME must unique, duplicates allowed. following table, show example entries list format. example entry list 12 treatments. Input number treatments Input # Treatments box. enter 45 sample experiment. Set number plots incomplete block Input # Plots per IBlock box. examples, set 5. Select number replications treatments Input # Full Reps box. examples, set 3. Enter number locations Input # Locations. run experiment single location, set 1. Select serpentine cartesian Plot Order Layout. example use default serpentine layout. Enter starting plot number Starting Plot Number box. experiment multiple locations, must enter comma separated list numbers length number locations input valid. Set 101. Enter name location experiment Input Location box. multiple locations, name must comma separated list. example, set \"FARGO\". ensure randomizations consistent across sessions, can set random seed box labeled random seed. example, set 1244. entered information experiment left side panel, click Run! button run design.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/row_column.html","id":"outputs","dir":"Articles","previous_headings":"","what":"Outputs","title":"Row-Column Design","text":"run row-column design FielDHub, several ways display information contained field book.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/row_column.html","id":"field-layout","dir":"Articles","previous_headings":"Outputs","what":"Field Layout","title":"Row-Column Design","text":"first click run button row-column design, FielDHub displays Field Layout tab, shows entries arrangement field. box display, can change layout field. can also display heatmap field changing Type Plot Heatmap. view heatmap, must first simulate experiment described field Simulate! button. pop-window appear can enter variable want simulate along minimum maximum values.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/row_column.html","id":"field-book","dir":"Articles","previous_headings":"Outputs","what":"Field Book","title":"Row-Column Design","text":"Field Book displays information experimental design table format. contains specific plot number row column address entry, well corresponding treatment plot. table searchable, can filter data relevant columns. simulated data heatmap, additional column variable appears field book.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/row_column.html","id":"using-the-fieldhub-function-row_column","dir":"Articles","previous_headings":"","what":"2. Using the FielDHub function: row_column()","title":"Row-Column Design","text":"can run design function FielDHub package, row_column(). First, need load FielDHub package typing , can enter information describing design like :","code":"library(FielDHub) rcd <- row_column( t = 45, nrows = 5, r = 3, l = 1, plotNumber = 101, locationNames = \"FARGO\", seed = 1244 )"},{"path":"https://didiermurillof.github.io/FielDHub/articles/row_column.html","id":"details-on-the-inputs-entered-in-row_column-above","dir":"Articles","previous_headings":"2. Using the FielDHub function: row_column()","what":"Details on the inputs entered in row_column() above","title":"Row-Column Design","text":"description inputs used generate design, t = 45 number treatments. nrows = 5 number rows. r=3 number reps l = 1 number locations. plotNumber = 101 starting plot number. locationNames = \"FARGO\" optional name location. seed = 1244 random seed replicate identical randomizations.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/row_column.html","id":"print-rcd-object","dir":"Articles","previous_headings":"2. Using the FielDHub function: row_column()","what":"Print rcd object","title":"Row-Column Design","text":"print summary information object rcd, can use generic function print().","code":"print(rcd) Resolvable Row-Column Design (Two-Step Optimization) Efficiency of design: Level Blocks D-Efficiency A-Efficiency A-Bound 1 Rep 3 1.0000000 1.0000000 1.0000000 2 Row 15 0.8940648 0.8767593 0.8842892 3 Column 27 0.7912269 0.7624155 0.7674419 Information on the design parameters: List of 7 $ rows : num 5 $ columns : num 9 $ reps : num 3 $ treatments : num 45 $ locations : num 1 $ location_names: chr \"FARGO\" $ seed : num 1244 10 First observations of the data frame with the row_column field book: ID LOCATION PLOT REP ROW COLUMN ENTRY TREATMENT 1 1 FARGO 101 1 1 1 23 G-23 6 2 FARGO 102 1 1 2 22 G-22 11 3 FARGO 103 1 1 3 28 G-28 16 4 FARGO 104 1 1 4 1 G-1 21 5 FARGO 105 1 1 5 13 G-13 26 6 FARGO 106 1 1 6 15 G-15 31 7 FARGO 107 1 1 7 37 G-37 36 8 FARGO 108 1 1 8 42 G-42 41 9 FARGO 109 1 1 9 39 G-39 2 10 FARGO 110 1 2 1 11 G-11"},{"path":"https://didiermurillof.github.io/FielDHub/articles/row_column.html","id":"access-to-rcd-object","dir":"Articles","previous_headings":"2. Using the FielDHub function: row_column()","what":"Access to rcd object","title":"Row-Column Design","text":"row_column() function returns list consisting information displayed output tabs FielDHub app: design information, plot layout, plot numbering, entries list, field book. accessible $ operator, .e.Β rcd$layoutRandom rcd$fieldBook. rcd$fieldBook list containing information every plot field, information location plot treatment plot. seen output , field book columns ID, LOCATION, PLOT, REP, ROW, COLUMN, ENTRY, TREATMENT.","code":"field_book <- rcd$fieldBook head(rcd$fieldBook, 10) ID LOCATION PLOT REP ROW COLUMN ENTRY TREATMENT 1 1 FARGO 101 1 1 1 23 G-23 6 2 FARGO 102 1 1 2 22 G-22 11 3 FARGO 103 1 1 3 28 G-28 16 4 FARGO 104 1 1 4 1 G-1 21 5 FARGO 105 1 1 5 13 G-13 26 6 FARGO 106 1 1 6 15 G-15 31 7 FARGO 107 1 1 7 37 G-37 36 8 FARGO 108 1 1 8 42 G-42 41 9 FARGO 109 1 1 9 39 G-39 2 10 FARGO 110 1 2 1 11 G-11"},{"path":"https://didiermurillof.github.io/FielDHub/articles/row_column.html","id":"plot-the-field-layout","dir":"Articles","previous_headings":"2. Using the FielDHub function: row_column()","what":"Plot the field layout","title":"Row-Column Design","text":"plotting layout function coordinates ROW COLUMN, can use generic function plot() follows,","code":"plot(rcd)"},{"path":"https://didiermurillof.github.io/FielDHub/articles/sparse_allocation.html","id":"sparse-allocationtesting","dir":"Articles","previous_headings":"","what":"Sparse Allocation/Testing","title":"Sparse Allocation","text":"vignette shows generate un-replicated designs leveraging sparse allocation method using FielDHub Shiny App scripting function sparse_allocation() FielDHub R package.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/sparse_allocation.html","id":"overview","dir":"Articles","previous_headings":"","what":"Overview","title":"Sparse Allocation","text":"Sparse allocation valuable strategy plant breeding experiments, allows researchers evaluate large number treatments multiple locations single experiment. Sparse allocation can increase efficiency reduce number experimental units required, making cost-effective option. One standard method implementing sparse allocation plant breeding experiments incomplete block designs (IBD) (Edmondson 2020). following key points summarize advantages disadvantages sparse allocation (Montesinos-Lopez et al. 2022): Increased efficiency: using sparse allocation, breeders can evaluate large number genotypes treatments single experiment across multiple environments, can accelerate breeding program reduce time resources needed evaluation. Selection intensity: large number genotypes treatments evaluated sparse allocation experiments can increase genetic diversity breeding program increase chances identifying superior genotypes treatments. Cost-effective: Sparse allocation experiments generally less expensive compared fully replicated experiments since fewer experimental units needed. Less accurate predictions: limited number experimental units means estimates treatment effects less precise compared fully replicated designs. However, increase selection intensity may compensate loss accuracy (Trade problem). FielDHub includes function run sparse allocation strategy multi-location randomization, well interface creating sparse allocation design FielDHub app.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/sparse_allocation.html","id":"use-case","dir":"Articles","previous_headings":"","what":"Use Case","title":"Sparse Allocation","text":"plant breeding project aims test 260 entries across five environments, due limited seed availability, four replications genotype can created across five locations. result, genotypes present environments. Additionally, project includes four checks replicated environments. address seed shortage, sparse allocation strategy used. table illustrates allocation first ten genotypes across five environments. table illustrates allocation genotypes across different environments, genotypes listed rows environments columns. Specifically, indicates Genotype 1 (Gen-1) assigned locations 1, 2, 3, 4, environment 5. process allocating genotypes locations achieved optimization process employs IBD principles.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/sparse_allocation.html","id":"running-the-shiny-app","dir":"Articles","previous_headings":"","what":"Running the Shiny App","title":"Sparse Allocation","text":"launch app need run either ","code":"FielDHub::run_app() library(FielDHub) run_app()"},{"path":"https://didiermurillof.github.io/FielDHub/articles/sparse_allocation.html","id":"using-the-fieldhub-shiny-app","dir":"Articles","previous_headings":"","what":"1. Using the FielDHub Shiny App","title":"Sparse Allocation","text":"app running, go Unreplicated Designs > Sparse Allocation , follow following steps show generate sparse allocation experiment.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/sparse_allocation.html","id":"inputs","dir":"Articles","previous_headings":"","what":"Inputs","title":"Sparse Allocation","text":"Import entries’ list? Choose whether import list entry numbers names genotypes treatments. selection , app generate synthetic data entries names treatment/genotypes based user inputs. selection Yes, entries list must fulfill specific format must .csv file. file must columns ENTRY NAME. ENTRY column must unique integer number entry treatment/genotype. column NAME must unique name identifies treatment/genotype. ENTRY NAME must unique, duplicates allowed. following table shows example entries list format. checks must appear first rows .csv file. Enter number entries/treatments Input # Entries box, 260 case. Select 4 drop-Input # Checks box. Since want run experiment 5 locations, set Input # Locations 5. Set number copies treatment # Copies Per Entry dropdown box 4. Select serpentine cartesian Plot Order Layout. example use serpentine layout. ensure randomizations consistent across sessions, can set seed number box labeled Random Seed. instance, set 16. Enter name experiment Input Experiment Name box. example, SparseTest2023. Enter starting plot number Starting Plot Number box. experiment want plot start 1, 1001, 2001, 3001, 4001 location. Enter name site/location Input Location box. experiment set sites FARGO, CASSELTON, MINOT, PROSPER, WILLISTON. entered information experiment left side panel, click Run! button run design. prompted select dimensions field list options drop-middle screen box labeled Select dimensions field. case, select 16 x 15. also can see table sparse allocation. Click Randomize! button randomize experiment set field dimensions see output plots. change inputs left side panel running experiment initially, click Run Randomize buttons , re-run new inputs.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/sparse_allocation.html","id":"outputs","dir":"Articles","previous_headings":"","what":"Outputs","title":"Sparse Allocation","text":"run sparse allocation design FielDHub set dimensions field, several ways display information sparse process randomization.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/sparse_allocation.html","id":"expt-design-info","dir":"Articles","previous_headings":"Outputs","what":"Expt Design Info","title":"Sparse Allocation","text":"first tab, Expt Design Info, can see entries randomization displayed binary matrix column location, 1 indicating respective genotype respective location, 0 indicating . sparse genotype allocation characteristic method. buttons copy, print, save table Excel file.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/sparse_allocation.html","id":"randomized-field","dir":"Articles","previous_headings":"Outputs","what":"Randomized Field","title":"Sparse Allocation","text":"Randomized Field tab displays graphical representation randomization entries field specified dimensions. checks colored uniquely, showing number times distributed throughout field. display includes numbered labels rows columns. can copy field table save directly Excel file Copy Excel buttons top. Choose % Checks: drop-box, users can play different options total amount checks field.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/sparse_allocation.html","id":"plot-number-field","dir":"Articles","previous_headings":"Outputs","what":"Plot Number Field","title":"Sparse Allocation","text":"Plot Number Field tab, table display field plots numbered according Plot Order Layout specified, either serpentine cartesian. can see corresponding entries plot number field book. Like Randomized Field tab, can copy table save Excel file Copy Excel buttons.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/sparse_allocation.html","id":"field-book","dir":"Articles","previous_headings":"Outputs","what":"Field Book","title":"Sparse Allocation","text":"Field Book displays information experimental design table format. contains specific plot number row column address entry, well corresponding treatment plot. table searchable, can filter data relevant columns.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/sparse_allocation.html","id":"using-the-fieldhub-function-sparse_allocation","dir":"Articles","previous_headings":"","what":"2. Using the FielDHub function: sparse_allocation()","title":"Sparse Allocation","text":"can run design function FielDHub package, sparse_allocation(). First, need load FielDHub package typing, , can enter information describing design like :","code":"library(FielDHub) sparse_example <- sparse_allocation( lines = 260, l = 5, copies_per_entry = 4, checks = 4, plotNumber = c(1, 1001, 2001, 3001, 4001), locationNames = c(\"FARGO\", \"CASSELTON\", \"MINOT\", \"PROSPER\", \"WILLISTON\"), exptName = \"SparseTest2023\", seed = 16 )"},{"path":"https://didiermurillof.github.io/FielDHub/articles/sparse_allocation.html","id":"details-on-the-inputs-entered-in-sparse_allocation-above","dir":"Articles","previous_headings":"2. Using the FielDHub function: sparse_allocation()","what":"Details on the inputs entered in sparse_allocation() above:","title":"Sparse Allocation","text":"lines = 260 number genotypes. l = 5 number locations. copies_per_entry = 4 number copies entry. checks = 4 number checks. plotNumber = c(1, 1001, 2001, 3001, 4001) optional starting plot numbers locationNames = c(\"FARGO\", \"CASSELTON\", \"MINOT\", \"PROSPER\", \"WILLISTON\") optional names location. exptName = \"SparseTest2023\" optional name experiment seed = 16 random seed number replicate identical randomizations.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/sparse_allocation.html","id":"print-sparse_example-object","dir":"Articles","previous_headings":"2. Using the FielDHub function: sparse_allocation()","what":"Print sparse_example object","title":"Sparse Allocation","text":"print summary information object sparse_example, can use generic function print(). sparse_allocation() function returns list objects, includes outputs function diagonal_arrangement() addition list_locs, allocation, size_locations. list_locs object list data frames. data frame two columns; ENTRY NAME information randomize environment. object allocation binary allocation matrix genotypes locations, size_locations data frame column location row indicating size location (number field plots). example, can display allocation object. Let us print first ten genotypes allocation. can manipulate sparse_allocation object list R. example, can print design information following: outputs:","code":"print(head(sparse_example$allocation, 10)) LOC1 LOC2 LOC3 LOC4 LOC5 1 1 1 0 1 1 2 1 1 1 0 1 3 1 1 0 1 1 4 1 1 1 1 0 5 1 1 1 0 1 6 1 1 0 1 1 7 1 0 1 1 1 8 0 1 1 1 1 9 1 1 1 1 0 10 1 1 1 0 1 print(sparse_example) Sparse Allocation: Un-replicated Diagonal Arrangement Design Information on the design parameters: List of 11 $ rows : num 15 $ columns : num 16 $ treatments : int 208 $ checks : int 4 $ entry_checks :List of 5 ..$ : num [1:4] 261 262 263 264 ..$ : num [1:4] 261 262 263 264 ..$ : num [1:4] 261 262 263 264 ..$ : num [1:4] 261 262 263 264 ..$ : num [1:4] 261 262 263 264 $ rep_checks :List of 5 ..$ : num [1:4] 8 7 7 8 ..$ : num [1:4] 8 8 7 7 ..$ : num [1:4] 7 7 8 8 ..$ : num [1:4] 7 8 7 8 ..$ : num [1:4] 8 7 7 8 $ locations : num 5 $ planter : chr \"serpentine\" $ percent_checks: chr [1:5] \"12.5%\" \"12.5%\" \"12.5%\" \"12.5%\" ... $ fillers : int 2 $ seed : num 16 10 First observations of the data frame with the diagonal_arrangement field book: ID EXPT LOCATION YEAR PLOT ROW COLUMN CHECKS ENTRY TREATMENT 1 1 SparseTest2023 FARGO 2024 1 1 1 0 34 G-34 2 2 SparseTest2023 FARGO 2024 2 1 2 0 83 G-83 3 3 SparseTest2023 FARGO 2024 3 1 3 0 59 G-59 4 4 SparseTest2023 FARGO 2024 4 1 4 261 261 CH-261 5 5 SparseTest2023 FARGO 2024 5 1 5 0 220 G-220 6 6 SparseTest2023 FARGO 2024 6 1 6 0 65 G-65 7 7 SparseTest2023 FARGO 2024 7 1 7 0 188 G-188 8 8 SparseTest2023 FARGO 2024 8 1 8 0 22 G-22 9 9 SparseTest2023 FARGO 2024 9 1 9 0 235 G-235 10 10 SparseTest2023 FARGO 2024 10 1 10 0 238 G-238"},{"path":"https://didiermurillof.github.io/FielDHub/articles/sparse_allocation.html","id":"access-to-sparse_example-object","dir":"Articles","previous_headings":"2. Using the FielDHub function: sparse_allocation()","what":"Access to sparse_example object","title":"Sparse Allocation","text":"object sparse_example list consisting information displayed output tabs FielDHub app: design information, plot layout, plot numbering, entries list, field book, indexed location experiment. accessible $ operator, .e.Β designs$layoutRandom[[1]] LOC1 designs$fieldBook whole field book. designs$fieldBook data frame containing information every plot field, information location plot treatment plot. seen output , field book columns ID, EXPT, LOCATION, YEAR, PLOT, ROW, COLUMN, CHECKS, ENTRY, TREATMENT. Let us see first 10 rows field book first location experiment.","code":"field_book <- sparse_example$fieldBook head(field_book, 10) ID EXPT LOCATION YEAR PLOT ROW COLUMN CHECKS ENTRY TREATMENT 1 1 SparseTest2023 FARGO 2024 1 1 1 0 34 G-34 2 2 SparseTest2023 FARGO 2024 2 1 2 0 83 G-83 3 3 SparseTest2023 FARGO 2024 3 1 3 0 59 G-59 4 4 SparseTest2023 FARGO 2024 4 1 4 261 261 CH-261 5 5 SparseTest2023 FARGO 2024 5 1 5 0 220 G-220 6 6 SparseTest2023 FARGO 2024 6 1 6 0 65 G-65 7 7 SparseTest2023 FARGO 2024 7 1 7 0 188 G-188 8 8 SparseTest2023 FARGO 2024 8 1 8 0 22 G-22 9 9 SparseTest2023 FARGO 2024 9 1 9 0 235 G-235 10 10 SparseTest2023 FARGO 2024 10 1 10 0 238 G-238"},{"path":[]},{"path":"https://didiermurillof.github.io/FielDHub/articles/sparse_allocation.html","id":"plot-field-layout-location-1","dir":"Articles","previous_headings":"2. Using the FielDHub function: sparse_allocation() > Plot field layout","what":"Plot field layout Location 1","title":"Sparse Allocation","text":"plotting layout function coordinates ROW COLUMN field book object can use generic function plot() follows, plot LOC1. can plot location experiment, like location 2 example:","code":"plot(sparse_example, l = 1)"},{"path":"https://didiermurillof.github.io/FielDHub/articles/sparse_allocation.html","id":"plot-field-layout-location-2","dir":"Articles","previous_headings":"2. Using the FielDHub function: sparse_allocation() > Plot field layout","what":"Plot field layout Location 2","title":"Sparse Allocation","text":"figure shows map experiment randomized unreplicated arrangement design. blue plots represent unreplicated treatments, yellow-boxed colored check plots replicated throughout field systematic diagonal arrangement. red plots 0s fillers.","code":"plot(sparse_example, l = 2)"},{"path":[]},{"path":"https://didiermurillof.github.io/FielDHub/articles/split_plot.html","id":"using-the-fieldhub-shiny-app","dir":"Articles","previous_headings":"","what":"1. Using the FielDHub Shiny App","title":"Split-Plot Design","text":"launch app need run either app running, go Designs > Split-Plot Design , follow following steps show generate kind design example 3 whole plots, 2 sub-plots 3 reps. run experiment just one location.","code":"FielDHub::run_app() library(FielDHub) run_app()"},{"path":"https://didiermurillof.github.io/FielDHub/articles/split_plot.html","id":"inputs","dir":"Articles","previous_headings":"","what":"Inputs","title":"Split-Plot Design","text":"selection , means app going generate synthetic data entries names treatment/genotypes based user inputs. selection Yes, entries list must fulfill specific format must .csv file. file must single column TREATMENT, containing list unique names identify treatment/genotype. Duplicate values allowed, entries must unique. following, show example entries list format. example entry list 5 whole-plots 3 sub-plots. Choose whether use split-plot design RCBD CRD Select SPD Type box. Set number whole-plots design Whole-plots box. Set 5. Set number sub-plots contained Sub-plots Within Whole-plots box. Set 3. Select number replications treatments Input # Full Reps box. Set 4. Enter number locations Input # Locations. run experiment single location, set 1. Select serpentine cartesian Plot Order Layout. example use default serpentine layout. Enter starting plot number Starting Plot Number box. experiment multiple locations, must enter comma separated list numbers length number locations input valid. case, set 101. Enter name location experiment Input Location box. multiple locations, name must comma separated list. Set \"FARGO\". ensure randomizations consistent across sessions, can set random seed box labeled random seed. example, set 1237. entered information experiment left side panel, click Run! button run design.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/split_plot.html","id":"outputs","dir":"Articles","previous_headings":"Inputs","what":"Outputs","title":"Split-Plot Design","text":"run split-plot design FielDHub, several ways display information contained field book.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/split_plot.html","id":"field-layout","dir":"Articles","previous_headings":"Inputs > Outputs","what":"Field Layout","title":"Split-Plot Design","text":"first click run button split-plot design, FielDHub displays Field Layout tab, shows entries arrangement field. box display, can change layout field. can also display heatmap field changing Type Plot Heatmap. view heatmap, must first simulate experiment described field Simulate! button. pop-window appear can enter variable want simulate along minimum maximum values.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/split_plot.html","id":"field-book","dir":"Articles","previous_headings":"Inputs > Outputs","what":"Field Book","title":"Split-Plot Design","text":"Field Book displays information experimental design table format. contains specific plot number row column address entry, well corresponding treatment plot. table searchable, can filter data relevant columns. simulated data heatmap, additional column variable appears field book.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/split_plot.html","id":"using-the-fieldhub-function-split_plot","dir":"Articles","previous_headings":"Inputs","what":"2. Using the FielDHub function: split_plot()","title":"Split-Plot Design","text":"can run design function FielDHub package, split_plot(). First, need load FielDHub package typing , can enter information describing design like :","code":"library(FielDHub) spd <- split_plot( wp = 5, sp = 3, reps = 4, type = 2, plotNumber = 101, locationNames = \"FARGO\", l = 1, seed = 1240 )"},{"path":"https://didiermurillof.github.io/FielDHub/articles/split_plot.html","id":"details-on-the-inputs-entered-in-split_plot-above","dir":"Articles","previous_headings":"Inputs > 2. Using the FielDHub function: split_plot()","what":"Details on the inputs entered in split_plot() above","title":"Split-Plot Design","text":"description inputs used generate design, wp = 5 number whole-plots. sp = 3 number sub-plots. reps = 4 number reps type = 2 CRD RCBD, 1 2 respectively l = 1 number locations. plotNumber = 101 starting plot number. locationNames = \"FARGO\" optional name location. seed = 1240 random seed replicate identical randomizations.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/split_plot.html","id":"print-spd-object","dir":"Articles","previous_headings":"Inputs > 2. Using the FielDHub function: split_plot()","what":"Print spd object","title":"Split-Plot Design","text":"","code":"print(spd) Split Plot Design Information on the design parameters: List of 7 $ WholePlots : int [1:5] 1 2 3 4 5 $ SubPlots : int [1:3] 1 2 3 $ locationNumber: num 1 $ locationNames : chr \"FARGO\" $ plotNumbers : num 101 $ typeDesign : chr \"RCBD\" $ seed : num 1240 10 First observations of the data frame with the split_plot field book: ID LOCATION PLOT REP WHOLE_PLOT SUB_PLOT TRT_COMB 1 1 FARGO 101 1 2 2 2|2 2 2 FARGO 101 1 2 1 2|1 3 3 FARGO 101 1 2 3 2|3 4 4 FARGO 102 1 4 3 4|3 5 5 FARGO 102 1 4 2 4|2 6 6 FARGO 102 1 4 1 4|1 7 7 FARGO 103 1 1 1 1|1 8 8 FARGO 103 1 1 3 1|3 9 9 FARGO 103 1 1 2 1|2 10 10 FARGO 104 1 3 3 3|3"},{"path":"https://didiermurillof.github.io/FielDHub/articles/split_plot.html","id":"access-to-spd-object","dir":"Articles","previous_headings":"Inputs > 2. Using the FielDHub function: split_plot()","what":"Access to spd object","title":"Split-Plot Design","text":"split_plot() function returns list consisting information displayed output tabs FielDHub app: design information, plot layout, plot numbering, entries list, field book. accessible $ operator, .e.Β spd$layoutRandom spd$fieldBook. spd$fieldBook list containing information every plot field, information location plot treatment plot. seen output , field book columns ID, LOCATION, PLOT, REP, WHOLE_PLOT, SUB_PLOT, TRT_COMB.","code":"field_book <- spd$fieldBook head(spd$fieldBook, 10) ID LOCATION PLOT REP WHOLE_PLOT SUB_PLOT TRT_COMB 1 1 FARGO 101 1 2 2 2|2 2 2 FARGO 101 1 2 1 2|1 3 3 FARGO 101 1 2 3 2|3 4 4 FARGO 102 1 4 3 4|3 5 5 FARGO 102 1 4 2 4|2 6 6 FARGO 102 1 4 1 4|1 7 7 FARGO 103 1 1 1 1|1 8 8 FARGO 103 1 1 3 1|3 9 9 FARGO 103 1 1 2 1|2 10 10 FARGO 104 1 3 3 3|3"},{"path":"https://didiermurillof.github.io/FielDHub/articles/split_plot.html","id":"plot-the-field-layout","dir":"Articles","previous_headings":"Inputs > 2. Using the FielDHub function: split_plot()","what":"Plot the field layout","title":"Split-Plot Design","text":"plotting layout function coordinates ROW COLUMN, can use generic function plot() follows,","code":"plot(spd)"},{"path":"https://didiermurillof.github.io/FielDHub/articles/split_split_plot.html","id":"using-the-fieldhub-shiny-app","dir":"Articles","previous_headings":"","what":"1. Using the FielDHub Shiny App","title":"Split-Split Plot Design","text":"launch app need run either app running, go Designs > Split-Split Plot Design , follow following steps show generate kind design example 3 whole plots, 2 sub-plots, 4 sub-sub plots 3 reps. run experiment just one location.","code":"FielDHub::run_app() library(FielDHub) run_app()"},{"path":"https://didiermurillof.github.io/FielDHub/articles/split_split_plot.html","id":"inputs","dir":"Articles","previous_headings":"","what":"Inputs","title":"Split-Split Plot Design","text":"selection , means app going generate synthetic data entries names treatment based user inputs. selection Yes, entries list must fulfill specific format must .csv file. file must single column TREATMENT, containing list unique names identify treatment. Duplicate values allowed, entries must unique. following, show example entries list format. example entry list 10 treatments. Choose whether use split-plot design RCBD CRD Select SPD Type box. Set number whole-plots design Whole-plots box. Set 3. Set number sub-plots contained Sub-plots Within Whole-plots box. Set 2. Set number sub-sub plots contained Sub-Sub-plots within Sub-plots box. Set 4. Select number replications treatments Input # Full Reps box. Set 3. Enter number locations Input # Locations. run experiment single location, set 1. Select serpentine cartesian Plot Order Layout. example use default serpentine layout. Enter starting plot number Starting Plot Number box. experiment multiple locations, must enter comma separated list numbers length number locations input valid. case, set 101. Enter name location experiment Input Location box. multiple locations, name must comma separated list. Set \"FARGO\". ensure randomizations consistent across sessions, can set random seed box labeled random seed. example, set 1238. entered information experiment left side panel, click Run! button run design.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/split_split_plot.html","id":"outputs","dir":"Articles","previous_headings":"Inputs","what":"Outputs","title":"Split-Split Plot Design","text":"run split-split-plot design FielDHub, several ways display information contained field book.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/split_split_plot.html","id":"field-layout","dir":"Articles","previous_headings":"Inputs > Outputs","what":"Field Layout","title":"Split-Split Plot Design","text":"first click run button split-split-plot design, FielDHub displays Field Layout tab, shows entries arrangement field. box display, can change layout field. can also display heatmap field changing Type Plot Heatmap. view heatmap, must first simulate experiment described field Simulate! button. pop-window appear can enter variable want simulate along minimum maximum values.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/split_split_plot.html","id":"field-book","dir":"Articles","previous_headings":"Inputs > Outputs","what":"Field Book","title":"Split-Split Plot Design","text":"Field Book displays information experimental design table format. contains specific plot number row column address entry, well corresponding treatment plot. table searchable, can filter data relevant columns. simulated data heatmap, additional column variable appears field book.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/split_split_plot.html","id":"using-the-fieldhub-function-split_split_plot","dir":"Articles","previous_headings":"Inputs","what":"2. Using the FielDHub function: split_split_plot()","title":"Split-Split Plot Design","text":"can run design function FielDHub package, split_split_plot(). First, need load FielDHub package typing, , can enter information describing design like :","code":"library(FielDHub) sspd <- split_split_plot( wp = 3, sp = 2, ssp = 4, reps = 3, type = 2, l = 1, plotNumber = 101, locationNames = \"FARGO\", seed = 123 )"},{"path":"https://didiermurillof.github.io/FielDHub/articles/split_split_plot.html","id":"details-on-the-inputs-entered-in-split_split_plot-above","dir":"Articles","previous_headings":"Inputs > 2. Using the FielDHub function: split_split_plot()","what":"Details on the inputs entered in split_split_plot() above","title":"Split-Split Plot Design","text":"description inputs used generate design, wp = 3 number whole-plots. sp = 2 number sub-plots. ssp = 4 number sub-sub-plots. reps = 3 number reps type = 2 CRD RCBD, 1 2 respectively l = 1 number locations. plotNumber = 101 starting plot number. locationNames = \"FARGO\" optional name location. seed = 1240 random seed replicate identical randomizations.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/split_split_plot.html","id":"print-sspd-object","dir":"Articles","previous_headings":"Inputs > 2. Using the FielDHub function: split_split_plot()","what":"Print sspd object","title":"Split-Split Plot Design","text":"","code":"print(sspd) Split-Split Plot Design Information on the design parameters: List of 6 $ Whole.Plots : int [1:3] 1 2 3 $ Sub.Plots : int [1:2] 1 2 $ Sub.Sub.Plots: int [1:4] 1 2 3 4 $ Locations : num 1 $ typeDesign : chr \"RCBD\" $ seed : num 123 10 First observations of the data frame with the split_split_plot field book: ID LOCATION PLOT REP WHOLE_PLOT SUB_PLOT SUB_SUB_PLOT TRT_COMB 1 1 FARGO 101 1 3 1 2 3|1|2 2 2 FARGO 101 1 3 1 3 3|1|3 3 3 FARGO 101 1 3 1 4 3|1|4 4 4 FARGO 101 1 3 1 1 3|1|1 5 5 FARGO 101 1 3 2 2 3|2|2 6 6 FARGO 101 1 3 2 3 3|2|3 7 7 FARGO 101 1 3 2 4 3|2|4 8 8 FARGO 101 1 3 2 1 3|2|1 9 9 FARGO 102 1 1 2 1 1|2|1 10 10 FARGO 102 1 1 2 3 1|2|3"},{"path":"https://didiermurillof.github.io/FielDHub/articles/split_split_plot.html","id":"access-to-sspd-object","dir":"Articles","previous_headings":"Inputs > 2. Using the FielDHub function: split_split_plot()","what":"Access to sspd object","title":"Split-Split Plot Design","text":"split_split_plot() function returns list consisting information displayed output tabs FielDHub app: design information, plot layout, plot numbering, entries list, field book. accessible $ operator, .e.Β sspd$layoutRandom sspd$fieldBook. sspd$fieldBook list containing information every plot field, information location plot treatment plot. seen output , field book columns ID, LOCATION, PLOT, REP, WHOLE_PLOT, SUB_PLOT, SUB_SUB_PLOT, TRT_COMB.","code":"field_book <- sspd$fieldBook head(field_book,10) ID LOCATION PLOT REP WHOLE_PLOT SUB_PLOT SUB_SUB_PLOT TRT_COMB 1 1 FARGO 101 1 3 1 2 3|1|2 2 2 FARGO 101 1 3 1 3 3|1|3 3 3 FARGO 101 1 3 1 4 3|1|4 4 4 FARGO 101 1 3 1 1 3|1|1 5 5 FARGO 101 1 3 2 2 3|2|2 6 6 FARGO 101 1 3 2 3 3|2|3 7 7 FARGO 101 1 3 2 4 3|2|4 8 8 FARGO 101 1 3 2 1 3|2|1 9 9 FARGO 102 1 1 2 1 1|2|1 10 10 FARGO 102 1 1 2 3 1|2|3"},{"path":"https://didiermurillof.github.io/FielDHub/articles/split_split_plot.html","id":"plot-the-field-layout","dir":"Articles","previous_headings":"Inputs > 2. Using the FielDHub function: split_split_plot()","what":"Plot the field layout","title":"Split-Split Plot Design","text":"plotting layout function coordinates ROW COLUMN, can use generic function plot() follows,","code":"plot(sspd)"},{"path":"https://didiermurillof.github.io/FielDHub/articles/square_lattice.html","id":"using-the-fieldhub-shiny-app","dir":"Articles","previous_headings":"","what":"1. Using the FielDHub Shiny App","title":"Square Lattice Design","text":"generate square lattice design using FielDHub app: First, go Lattice Designs > Square Lattice , follow following steps show generate square lattice design 64 treatments 3 reps.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/square_lattice.html","id":"inputs","dir":"Articles","previous_headings":"","what":"Inputs","title":"Square Lattice Design","text":"selection , means app going generate synthetic data entries names treatment/genotypes based user inputs. selection Yes, entries list must fulfill specific format must .csv file. file must columns ENTRY NAME. ENTRY column must unique entry integer number treatment. column NAME must unique name identifies treatment. ENTRY NAME must unique, duplicates allowed. following table, show example entries list format. example entry list 12 treatments. Input number treatments Input # Treatments box. number treatments must square number square lattice design. enter 64 sample experiment. Select number replications treatments Input # Full Reps box. examples, set 3. Set number plots incomplete block Input # Plots per IBlock box. examples, set 8. Enter number locations Input # Locations. run experiment single location, set 1. Select serpentine cartesian Plot Order Layout. example use default serpentine layout. Enter starting plot number Starting Plot Number box. experiment multiple locations, must enter comma separated list numbers length number locations input valid. Set 101. Enter name location experiment Input Location box. multiple locations, name must comma separated list. example, set \"FARGO\". ensure randomizations consistent across sessions, can set random seed box labeled random seed. example, set 1233. entered information experiment left side panel, click Run! button run design.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/square_lattice.html","id":"outputs","dir":"Articles","previous_headings":"","what":"Outputs","title":"Square Lattice Design","text":"run square lattice design FielDHub, several ways display information contained field book.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/square_lattice.html","id":"field-layout","dir":"Articles","previous_headings":"Outputs","what":"Field Layout","title":"Square Lattice Design","text":"first click run button square lattice design, FielDHub displays Field Layout tab, shows entries arrangement field. box display, can change layout field change location displayed. can also display heatmap field changing Type Plot Heatmap. view heatmap, must first simulate experiment described field Simulate! button. pop-window appear can enter variable want simulate along minimum maximum values.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/square_lattice.html","id":"field-book","dir":"Articles","previous_headings":"Outputs","what":"Field Book","title":"Square Lattice Design","text":"Field Book displays information experimental design table format. contains specific plot number row column address entry, well corresponding treatment plot. table searchable, can filter data relevant columns. simulated data heatmap, additional column variable appears field book.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/square_lattice.html","id":"using-the-fieldhub-function-square_lattice","dir":"Articles","previous_headings":"","what":"2. Using the FielDHub function: square_lattice()","title":"Square Lattice Design","text":"can run design function FielDHub package, square_lattice(). First, need load FielDHub package typing, , can enter information describing design like :","code":"library(FielDHub) square <- square_lattice( t = 64, r = 3, k = 8, l = 1, plotNumber = 101, locationNames = \"FARGO\", seed = 1233 )"},{"path":"https://didiermurillof.github.io/FielDHub/articles/square_lattice.html","id":"details-on-the-inputs-entered-in-square_lattice-above","dir":"Articles","previous_headings":"2. Using the FielDHub function: square_lattice()","what":"Details on the inputs entered in square_lattice() above","title":"Square Lattice Design","text":"description inputs used generate design, t = 64 number treatments, must square number. r=3 number replicates. k = 8 number plots per incomplete block. l = 1 number locations. plotNumber = 101 starting plot number. locationNames = \"FARGO\" optional name location. seed = 1233 random seed replicate identical randomizations.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/square_lattice.html","id":"print-square-object","dir":"Articles","previous_headings":"2. Using the FielDHub function: square_lattice()","what":"Print square object","title":"Square Lattice Design","text":"print summary information object square, can use generic function print().","code":"print(square) Square Lattice Design Efficiency of design: Level Blocks D-Efficiency A-Efficiency A-Bound 1 1 3 1.0000000 1.0000000 1.0000000 2 2 24 0.8735805 0.8571429 0.8571429 Information on the design parameters: List of 7 $ Reps : num 3 $ IBlocks : num 8 $ NumberTreatments: num 64 $ NumberLocations : num 1 $ Locations : chr \"FARGO\" $ seed : num 1233 $ lambda : num 0.333 10 First observations of the data frame with the square_lattice field book: ID LOCATION PLOT REP IBLOCK UNIT ENTRY TREATMENT 1 1 FARGO 101 1 1 1 3 G-3 2 2 FARGO 102 1 1 2 38 G-38 3 3 FARGO 103 1 1 3 37 G-37 4 4 FARGO 104 1 1 4 24 G-24 5 5 FARGO 105 1 1 5 40 G-40 6 6 FARGO 106 1 1 6 29 G-29 7 7 FARGO 107 1 1 7 25 G-25 8 8 FARGO 108 1 1 8 53 G-53 9 9 FARGO 109 1 2 1 61 G-61 10 10 FARGO 110 1 2 2 23 G-23"},{"path":"https://didiermurillof.github.io/FielDHub/articles/square_lattice.html","id":"access-to-square-object","dir":"Articles","previous_headings":"2. Using the FielDHub function: square_lattice()","what":"Access to square object","title":"Square Lattice Design","text":"square_lattice() function returns list consisting information displayed output tabs FielDHub app: design information, plot layout, plot numbering, entries list, field book. accessible $ operator, .e.Β square$layoutRandom square$fieldBook. square$fieldBook list containing information every plot field, information location plot treatment plot. seen output , field book columns ID, LOCATION, PLOT, REP, IBLOCK, UNIT, ENTRY, TREATMENT.","code":"field_book <- square$fieldBook head(square$fieldBook, 10) ID LOCATION PLOT REP IBLOCK UNIT ENTRY TREATMENT 1 1 FARGO 101 1 1 1 3 G-3 2 2 FARGO 102 1 1 2 38 G-38 3 3 FARGO 103 1 1 3 37 G-37 4 4 FARGO 104 1 1 4 24 G-24 5 5 FARGO 105 1 1 5 40 G-40 6 6 FARGO 106 1 1 6 29 G-29 7 7 FARGO 107 1 1 7 25 G-25 8 8 FARGO 108 1 1 8 53 G-53 9 9 FARGO 109 1 2 1 61 G-61 10 10 FARGO 110 1 2 2 23 G-23"},{"path":"https://didiermurillof.github.io/FielDHub/articles/square_lattice.html","id":"plot-the-field-layout","dir":"Articles","previous_headings":"2. Using the FielDHub function: square_lattice()","what":"Plot the field layout","title":"Square Lattice Design","text":"plotting layout function coordinates ROW COLUMN, can use generic function plot() follows,","code":"plot(square)"},{"path":"https://didiermurillof.github.io/FielDHub/articles/strip_plot.html","id":"using-the-fieldhub-shiny-app","dir":"Articles","previous_headings":"","what":"1. Using the FielDHub Shiny App","title":"Strip-Plot Design","text":"launch app need run either app running, go Designs > Strip-Plot Design , follow following steps show generate kind design example 6 factors horizontal strips, 4 factors vertical strips 3 reps. run experiment just one location.","code":"FielDHub::run_app() library(FielDHub) run_app()"},{"path":"https://didiermurillof.github.io/FielDHub/articles/strip_plot.html","id":"inputs","dir":"Articles","previous_headings":"","what":"Inputs","title":"Strip-Plot Design","text":"selection , means app going generate synthetic data entries names treatment based user inputs. selection Yes, entries list must fulfill specific format must .csv file. file must single column TREATMENT, containing list unique names identify treatment. Duplicate values allowed, entries must unique. following, show example entries list format. example entry list 10 treatments. Input number factors horizontal strips Input # Horizontal Strips box. Set 6. Input number factors vertical strips Input # Vertical Strips box. Set 4. Select number replications experiment Input # Full Reps box. Set 3. Enter number locations Input # Locations. run experiment single location, set 1. Select serpentine cartesian Plot Order Layout. example use default serpentine layout. Enter starting plot number Starting Plot Number box. experiment multiple locations, must enter comma separated list numbers length number locations input valid. case, set 101. Enter name location experiment Input Location box. multiple locations, name must comma separated list. Set \"FARGO\". ensure randomizations consistent across sessions, can set random seed box labeled random seed. example, set 1237. entered information experiment left side panel, click Run! button run design.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/strip_plot.html","id":"outputs","dir":"Articles","previous_headings":"","what":"Outputs","title":"Strip-Plot Design","text":"run strip-plot design FielDHub, several ways display information contained field book.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/strip_plot.html","id":"field-layout","dir":"Articles","previous_headings":"Outputs","what":"Field Layout","title":"Strip-Plot Design","text":"first click run button strip-plot design, FielDHub displays Field Layout tab, shows entries arrangement field. box display, can change layout field. can also display heatmap field changing Type Plot Heatmap. view heatmap, must first simulate experiment described field Simulate! button. pop-window appear can enter variable want simulate along minimum maximum values.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/strip_plot.html","id":"field-book","dir":"Articles","previous_headings":"Outputs","what":"Field Book","title":"Strip-Plot Design","text":"Field Book displays information experimental design table format. contains specific plot number row column address entry, well corresponding treatment plot. table searchable, can filter data relevant columns. simulated data heatmap, additional column variable appears Field Book.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/strip_plot.html","id":"using-the-fieldhub-function-strip_plot","dir":"Articles","previous_headings":"","what":"2. Using the FielDHub function: strip_plot()","title":"Strip-Plot Design","text":"can run design function FielDHub package, strip_plot(). can enter information describing design like : can run design function FielDHub package, strip_plot(). First, need load FielDHub package typing, , can enter information describing design like :","code":"library(FielDHub) strip <- strip_plot( Hplots = 6, Vplots = 4, b = 3, l = 1, plotNumber = 101, planter = \"serpentine\", locationNames = \"FARGO\", seed = 1240 )"},{"path":"https://didiermurillof.github.io/FielDHub/articles/strip_plot.html","id":"details-on-the-inputs-entered-in-strip_plot-above","dir":"Articles","previous_headings":"2. Using the FielDHub function: strip_plot()","what":"Details on the inputs entered in strip_plot() above","title":"Strip-Plot Design","text":"description inputs used generate design, Hplots = 6 number horizontal strips Vplots = 4 number vertical strips b = 3 number reps l = 1 number locations. plotNumber = 101 starting plot number. planter = \"cartesian\" order layout. locationNames = \"FARGO\" optional name location. seed = 1240 random seed replicate identical randomizations.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/articles/strip_plot.html","id":"print-strip-object","dir":"Articles","previous_headings":"2. Using the FielDHub function: strip_plot()","what":"Print strip object","title":"Strip-Plot Design","text":"","code":"print(strip) Strip Plot Design Information on the design parameters: List of 6 $ Hplots : int 6 $ Vplots : int 4 $ blocks : num 3 $ numberLocations: num 1 $ nameLocations : chr \"FARGO\" $ seed : num 1240 10 First observations of the data frame with the strip_plot field book: ID LOCATION PLOT REP HSTRIP VSTRIP TRT_COMB 1 1 FARGO 101 1 b1 a0 b1|a0 2 2 FARGO 102 1 b1 a1 b1|a1 3 3 FARGO 103 1 b1 a3 b1|a3 4 4 FARGO 104 1 b1 a2 b1|a2 5 5 FARGO 108 1 b3 a0 b3|a0 6 6 FARGO 107 1 b3 a1 b3|a1 7 7 FARGO 106 1 b3 a3 b3|a3 8 8 FARGO 105 1 b3 a2 b3|a2 9 9 FARGO 109 1 b4 a0 b4|a0 10 10 FARGO 110 1 b4 a1 b4|a1"},{"path":"https://didiermurillof.github.io/FielDHub/articles/strip_plot.html","id":"access-to-strip-object","dir":"Articles","previous_headings":"2. Using the FielDHub function: strip_plot()","what":"Access to strip object","title":"Strip-Plot Design","text":"strip_plot() function returns list consisting information displayed output tabs FielDHub app: design information, plot layout, plot numbering, entries list, field book. accessible $ operator, .e.Β strip$layoutRandom strip$fieldBook. strip$fieldBook list containing information every plot field, information location plot treatment plot. seen output , field book columns ID, LOCATION, PLOT, REP, HSTRIP, VSTRIP, TRT_COMB.","code":"field_book <- strip$fieldBook head(strip$fieldBook, 10) ID LOCATION PLOT REP HSTRIP VSTRIP TRT_COMB 1 1 FARGO 101 1 b1 a0 b1|a0 2 2 FARGO 102 1 b1 a1 b1|a1 3 3 FARGO 103 1 b1 a3 b1|a3 4 4 FARGO 104 1 b1 a2 b1|a2 5 5 FARGO 108 1 b3 a0 b3|a0 6 6 FARGO 107 1 b3 a1 b3|a1 7 7 FARGO 106 1 b3 a3 b3|a3 8 8 FARGO 105 1 b3 a2 b3|a2 9 9 FARGO 109 1 b4 a0 b4|a0 10 10 FARGO 110 1 b4 a1 b4|a1"},{"path":"https://didiermurillof.github.io/FielDHub/articles/strip_plot.html","id":"plot-the-field-layout","dir":"Articles","previous_headings":"2. Using the FielDHub function: strip_plot()","what":"Plot the field layout","title":"Strip-Plot Design","text":"plotting layout function coordinates ROW COLUMN, can use generic function plot() follow,","code":"plot(strip)"},{"path":"https://didiermurillof.github.io/FielDHub/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Didier Murillo. Maintainer, author. Salvador Gezan. Author. Ana Heilman. Contributor. Thomas Walk. Contributor. Johan Aparicio. Contributor. Matthew Seefeldt. Contributor. Jean-Marc Montpetit. Contributor. Richard Horsley. Contributor. North Dakota State University. Copyright holder.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Murillo D, Gezan S (2024). FielDHub: Shiny App Design Experiments Life Sciences. R package version 1.4.2, https://didiermurillof.github.io/FielDHub/, https://github.com/DidierMurilloF/FielDHub.","code":"@Manual{, title = {FielDHub: A Shiny App for Design of Experiments in Life Sciences}, author = {Didier Murillo and Salvador Gezan}, year = {2024}, note = {R package version 1.4.2, https://didiermurillof.github.io/FielDHub/}, url = {https://github.com/DidierMurilloF/FielDHub}, }"},{"path":[]},{"path":"https://didiermurillof.github.io/FielDHub/index.html","id":"overview","dir":"","previous_headings":"","what":"Overview","title":"A Shiny App for Design of Experiments in Life Sciences","text":"FielDHub R package/shiny design experiments (DOE) app aids creation traditional, un-replicated, augmented partially-replicated designs applied agriculture, plant breeding, forestry, animal biological sciences. details examples functions present FielDHub package. Please, go https://didiermurillof.github.io/FielDHub/reference/index.html.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/index.html","id":"usage","dir":"","previous_headings":"","what":"Usage","title":"A Shiny App for Design of Experiments in Life Sciences","text":"basic example shows launch app:","code":"library(FielDHub) run_app()"},{"path":"https://didiermurillof.github.io/FielDHub/index.html","id":"diagonal-arrangement-example","dir":"","previous_headings":"Usage","what":"Diagonal Arrangement Example","title":"A Shiny App for Design of Experiments in Life Sciences","text":"project needs test 280 genotypes field containing 16 rows 20 columns plots. example, 280 genotypes divided among three different experiments. addition, four checks included systematic diagonal arrangement across experiments fill 40 plots representing 12.5% total number experimental plots. option include filler plots also available fields number experimental plots equal number available field plots. figure shows map experiment randomized along multiple experiments (three) checks diagonals. Distinctively colored check plots replicated throughout field systematic diagonal arrangement. figure shows layout three experiments field.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/index.html","id":"using-the-fieldhub-function-diagonal_arrangement","dir":"","previous_headings":"Usage","what":"Using the FielDHub function diagonal_arrangement()","title":"A Shiny App for Design of Experiments in Life Sciences","text":"illustrate using FielDHub build experimental designs R code, design produced R Shiny interface described can also created using function diagonal_arrangement() R script . Note, obtain identical results, users must include random seed script used Shiny app. case, random seed 1249. Users can print returned values diagonal_arrangement() follow, First 12 rows fieldbook, Users can plot layout design diagonal_arrangement() using function plot() follows, figure, salmon, green, blue shade blocks unreplicated experiments, distinctively colored check plots replicated throughout field systematic diagonal arrangement. main difference using FielDHub Shiny app using standalone function diagonal_arrangement() standalone function allocate filler necessary, Shiny App, users can customize number fillers needed. cases users include fillers, either experiments, Shiny app preferable filling visualizing field plots. see examples, go https://didiermurillof.github.io/FielDHub/articles/diagonal_arrangement.html","code":"diagonal <- diagonal_arrangement( nrows = 16, ncols = 20, lines = 280, checks = 4, plotNumber = 101, splitBy = \"row\", seed = 1249, kindExpt = \"DBUDC\", blocks = c(100, 100, 80), exptName = c(\"Expt1\", \"Expt2\", \"Expt3\") ) print(diagonal) Un-replicated Diagonal Arrangement Design Information on the design parameters: List of 11 $ rows : num 16 $ columns : num 20 $ treatments : num [1:3] 100 100 80 $ checks : int 4 $ entry_checks :List of 1 ..$ : int [1:4] 1 2 3 4 $ rep_checks :List of 1 ..$ : num [1:4] 10 10 10 10 $ locations : num 1 $ planter : chr \"serpentine\" $ percent_checks: chr \"12.5%\" $ fillers : num 0 $ seed : num 1249 10 First observations of the data frame with the diagonal_arrangement field book: ID EXPT LOCATION YEAR PLOT ROW COLUMN CHECKS ENTRY TREATMENT 1 1 Expt1 1 2023 101 1 1 0 42 Gen-42 2 2 Expt1 1 2023 102 1 2 0 23 Gen-23 3 3 Expt1 1 2023 103 1 3 0 10 Gen-10 4 4 Expt1 1 2023 104 1 4 0 45 Gen-45 5 5 Expt1 1 2023 105 1 5 0 51 Gen-51 6 6 Expt1 1 2023 106 1 6 0 13 Gen-13 7 7 Expt1 1 2023 107 1 7 3 3 Check-3 8 8 Expt1 1 2023 108 1 8 0 43 Gen-43 9 9 Expt1 1 2023 109 1 9 0 84 Gen-84 10 10 Expt1 1 2023 110 1 10 0 102 Gen-102 head(diagonal$fieldBook, 12) ID EXPT LOCATION YEAR PLOT ROW COLUMN CHECKS ENTRY TREATMENT 1 1 Expt1 1 2023 101 1 1 0 42 Gen-42 2 2 Expt1 1 2023 102 1 2 0 23 Gen-23 3 3 Expt1 1 2023 103 1 3 0 10 Gen-10 4 4 Expt1 1 2023 104 1 4 0 45 Gen-45 5 5 Expt1 1 2023 105 1 5 0 51 Gen-51 6 6 Expt1 1 2023 106 1 6 0 13 Gen-13 7 7 Expt1 1 2023 107 1 7 3 3 Check-3 8 8 Expt1 1 2023 108 1 8 0 43 Gen-43 9 9 Expt1 1 2023 109 1 9 0 84 Gen-84 10 10 Expt1 1 2023 110 1 10 0 102 Gen-102 11 11 Expt1 1 2023 111 1 11 0 89 Gen-89 12 12 Expt1 1 2023 112 1 12 0 75 Gen-75 plot(diagonal)"},{"path":"https://didiermurillof.github.io/FielDHub/index.html","id":"partially-replicated-design-example","dir":"","previous_headings":"Usage","what":"Partially Replicated Design Example","title":"A Shiny App for Design of Experiments in Life Sciences","text":"Partially replicated designs commonly employed early generation field trials. type design characterized replication portion entries, remaining entries appearing experiment. example, considered field trial 288 plots containing 75 entries appearing two times , 138 entries appearing . field trials arranged field 16 rows 18 columns. figure , green plots contain replicated entries, plots contain entries appear .","code":""},{"path":"https://didiermurillof.github.io/FielDHub/index.html","id":"using-the-fieldhub-function-partially_replicated","dir":"","previous_headings":"Usage","what":"Using the FielDHub function partially_replicated()","title":"A Shiny App for Design of Experiments in Life Sciences","text":"Instead using Shiny FielDHub app, users can use standalone FielDHub function partially_replicated(). partially replicated layout described can produced scripting follows. noted previous example, obtain identical results script Shiny app, users need use random seed, , case, 77. Users can print returned values partially_replicated() follows, First 12 rows fieldbook, Users can plot layout design partially_replicated() using function plot() follows, see examples, please go https://didiermurillof.github.io/FielDHub/articles/partially_replicated.html","code":"pREP <- partially_replicated( nrows = 16, ncols = 18, repGens = c(138,75), repUnits = c(1,2), planter = \"serpentine\", plotNumber = 1, exptName = \"ExptA\", locationNames = \"FARGO\", seed = 77 ) print(pREP) Partially Replicated Design Replications within location: LOCATION Replicated Unreplicated 1 FARGO 75 138 Information on the design parameters: List of 7 $ rows : num 16 $ columns : num 18 $ min_distance : num 8 $ incidence_in_rows: num 3 $ locations : num 1 $ planter : chr \"serpentine\" $ seed : num 77 10 First observations of the data frame with the partially_replicated field book: ID EXPT LOCATION YEAR PLOT ROW COLUMN CHECKS ENTRY TREATMENT 1 1 ExptA FARGO 2023 1 1 1 30 30 G30 2 2 ExptA FARGO 2023 2 1 2 0 192 G192 3 3 ExptA FARGO 2023 3 1 3 44 44 G44 4 4 ExptA FARGO 2023 4 1 4 66 66 G66 5 5 ExptA FARGO 2023 5 1 5 0 78 G78 6 6 ExptA FARGO 2023 6 1 6 0 186 G186 7 7 ExptA FARGO 2023 7 1 7 34 34 G34 8 8 ExptA FARGO 2023 8 1 8 0 86 G86 9 9 ExptA FARGO 2023 9 1 9 37 37 G37 10 10 ExptA FARGO 2023 10 1 10 55 55 G55 head(pREP$fieldBook, 12) ID EXPT LOCATION YEAR PLOT ROW COLUMN CHECKS ENTRY TREATMENT 1 1 ExptA FARGO 2023 1 1 1 30 30 G30 2 2 ExptA FARGO 2023 2 1 2 0 192 G192 3 3 ExptA FARGO 2023 3 1 3 44 44 G44 4 4 ExptA FARGO 2023 4 1 4 66 66 G66 5 5 ExptA FARGO 2023 5 1 5 0 78 G78 6 6 ExptA FARGO 2023 6 1 6 0 186 G186 7 7 ExptA FARGO 2023 7 1 7 34 34 G34 8 8 ExptA FARGO 2023 8 1 8 0 86 G86 9 9 ExptA FARGO 2023 9 1 9 37 37 G37 10 10 ExptA FARGO 2023 10 1 10 55 55 G55 11 11 ExptA FARGO 2023 11 1 11 0 125 G125 12 12 ExptA FARGO 2023 12 1 12 0 159 G159 plot(pREP)"},{"path":"https://didiermurillof.github.io/FielDHub/reference/CRD.html","id":null,"dir":"Reference","previous_headings":"","what":"Generates a Completely Randomized Design (CRD) β€” CRD","title":"Generates a Completely Randomized Design (CRD) β€” CRD","text":"randomly generates completely randomized design.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/CRD.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generates a Completely Randomized Design (CRD) β€” CRD","text":"","code":"CRD( t = NULL, reps = NULL, plotNumber = 101, locationName = NULL, seed = NULL, data = NULL )"},{"path":"https://didiermurillof.github.io/FielDHub/reference/CRD.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generates a Completely Randomized Design (CRD) β€” CRD","text":"t integer number total number treatments vector dimension t labels. reps Number replicates treatment. plotNumber Starting plot number. default plotNumber = 101. locationName (optional) Name location. seed (optional) Real number specifies starting seed obtain reproducible designs. data (optional) Data frame 2 columns labels treatments number replicates.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/CRD.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generates a Completely Randomized Design (CRD) β€” CRD","text":"list two elements. infoDesign list information design parameters. fieldBook data frame CRD field book.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/CRD.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Generates a Completely Randomized Design (CRD) β€” CRD","text":"Federer, W. T. (1955). Experimental Design. Theory Application. New York, USA. Macmillan Company.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/CRD.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Generates a Completely Randomized Design (CRD) β€” CRD","text":"Didier Murillo [aut], Salvador Gezan [aut], Ana Heilman [ctb], Thomas Walk [ctb], Johan Aparicio [ctb], Richard Horsley [ctb]","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/CRD.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generates a Completely Randomized Design (CRD) β€” CRD","text":"","code":"# Example 1: Generates a CRD design with 10 treatments and 5 reps each. crd1 <- CRD( t = 10, reps = 5, plotNumber = 101, seed = 1987, locationName = \"Fargo\" ) crd1$infoDesign #> $numberofTreatments #> [1] 10 #> #> $treatments #> [1] \"T1\" \"T2\" \"T3\" \"T4\" \"T5\" \"T6\" \"T7\" \"T8\" \"T9\" \"T10\" #> #> $Reps #> [1] 5 #> #> $locationName #> [1] \"Fargo\" #> #> $seed #> [1] 1987 #> #> $id_design #> [1] 1 #> head(crd1$fieldBook, 10) #> ID LOCATION PLOT REP TREATMENT #> 1 1 Fargo 101 1 T10 #> 2 2 Fargo 102 4 T1 #> 3 3 Fargo 103 3 T3 #> 4 4 Fargo 104 3 T9 #> 5 5 Fargo 105 4 T3 #> 6 6 Fargo 106 5 T3 #> 7 7 Fargo 107 1 T2 #> 8 8 Fargo 108 3 T4 #> 9 9 Fargo 109 1 T1 #> 10 10 Fargo 110 3 T1 # Example 2: Generates a CRD design with 15 treatments and 6 reps each. Gens <- paste(\"Wheat\", 1:15, sep = \"\") crd2 <- CRD( t = Gens, reps = 6, plotNumber = 1001, seed = 1654, locationName = \"Fargo\" ) crd2$infoDesign #> $numberofTreatments #> [1] 15 #> #> $treatments #> [1] \"Wheat1\" \"Wheat2\" \"Wheat3\" \"Wheat4\" \"Wheat5\" \"Wheat6\" \"Wheat7\" #> [8] \"Wheat8\" \"Wheat9\" \"Wheat10\" \"Wheat11\" \"Wheat12\" \"Wheat13\" \"Wheat14\" #> [15] \"Wheat15\" #> #> $Reps #> [1] 6 #> #> $locationName #> [1] \"Fargo\" #> #> $seed #> [1] 1654 #> #> $id_design #> [1] 1 #> head(crd2$fieldBook, 10) #> ID LOCATION PLOT REP TREATMENT #> 1 1 Fargo 1001 6 Wheat3 #> 2 2 Fargo 1002 2 Wheat8 #> 3 3 Fargo 1003 2 Wheat2 #> 4 4 Fargo 1004 4 Wheat4 #> 5 5 Fargo 1005 1 Wheat1 #> 6 6 Fargo 1006 1 Wheat4 #> 7 7 Fargo 1007 1 Wheat13 #> 8 8 Fargo 1008 1 Wheat1 #> 9 9 Fargo 1009 6 Wheat15 #> 10 10 Fargo 1010 4 Wheat7 # Example 3: Generates a CRD design with 12 treatments and 4 reps each. # In this case, we show how to use the option data. treatments <- paste(\"ND-\", 1:12, sep = \"\") treatment_list <- data.frame(list(TREATMENT = treatments, REP = 4)) head(treatment_list) #> TREATMENT REP #> 1 ND-1 4 #> 2 ND-2 4 #> 3 ND-3 4 #> 4 ND-4 4 #> 5 ND-5 4 #> 6 ND-6 4 crd3 <- CRD( t = NULL, reps = NULL, plotNumber = 2001, seed = 1655, locationName = \"Cali\", data = treatment_list ) crd3$infoDesign #> $numberofTreatments #> [1] 12 #> #> $treatments #> [1] \"ND-1\" \"ND-2\" \"ND-3\" \"ND-4\" \"ND-5\" \"ND-6\" \"ND-7\" \"ND-8\" \"ND-9\" #> [10] \"ND-10\" \"ND-11\" \"ND-12\" #> #> $Reps #> [1] 4 4 4 4 4 4 4 4 4 4 4 4 #> #> $locationName #> [1] \"Cali\" #> #> $seed #> [1] 1655 #> #> $id_design #> [1] 1 #> head(crd3$fieldBook, 10) #> ID LOCATION PLOT REP TREATMENT #> 1 1 Cali 2001 4 ND-3 #> 2 2 Cali 2002 1 ND-7 #> 3 3 Cali 2003 2 ND-2 #> 4 4 Cali 2004 3 ND-8 #> 5 5 Cali 2005 3 ND-2 #> 6 6 Cali 2006 1 ND-10 #> 7 7 Cali 2007 3 ND-7 #> 8 8 Cali 2008 4 ND-9 #> 9 9 Cali 2009 3 ND-4 #> 10 10 Cali 2010 1 ND-3"},{"path":"https://didiermurillof.github.io/FielDHub/reference/RCBD.html","id":null,"dir":"Reference","previous_headings":"","what":"Generates a Randomized Complete Block Design (RCBD) β€” RCBD","title":"Generates a Randomized Complete Block Design (RCBD) β€” RCBD","text":"randomly generates randomized complete block design (RCBD) across locations.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/RCBD.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generates a Randomized Complete Block Design (RCBD) β€” RCBD","text":"","code":"RCBD( t = NULL, reps = NULL, l = 1, plotNumber = 101, continuous = FALSE, planter = \"serpentine\", seed = NULL, locationNames = NULL, data = NULL )"},{"path":"https://didiermurillof.github.io/FielDHub/reference/RCBD.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generates a Randomized Complete Block Design (RCBD) β€” RCBD","text":"t integer number total number treatments vector dimension t labels. reps Number replicates (full blocks) treatment. l Number locations. default l = 1. plotNumber Numeric vector starting plot number location. default plotNumber = 101. continuous Logical value plot number continuous . default continuous = FALSE. planter Option serpentine cartesian arrangement. default planter = 'serpentine'. seed (optional) Real number specifies starting seed obtain reproducible designs. locationNames (optional) Names location. data (optional) Data frame labels treatments.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/RCBD.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generates a Randomized Complete Block Design (RCBD) β€” RCBD","text":"list five elements. infoDesign list information design parameters. layoutRandom RCBD layout randomization location. plotNumber plot number layout location. fieldBook data frame RCBD field book design.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/RCBD.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Generates a Randomized Complete Block Design (RCBD) β€” RCBD","text":"Federer, W. T. (1955). Experimental Design. Theory Application. New York, USA. Macmillan Company.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/RCBD.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Generates a Randomized Complete Block Design (RCBD) β€” RCBD","text":"Didier Murillo [aut], Salvador Gezan [aut], Ana Heilman [ctb], Thomas Walk [ctb], Johan Aparicio [ctb], Richard Horsley [ctb]","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/RCBD.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generates a Randomized Complete Block Design (RCBD) β€” RCBD","text":"","code":"# Example 1: Generates a RCBD design with 3 blocks and 20 treatments across 3 locations. rcbd1 <- RCBD(t = LETTERS[1:20], reps = 5, l = 3, plotNumber = c(101,1001, 2001), continuous = TRUE, planter = \"serpentine\", seed = 1020, locationNames = c(\"FARGO\", \"MINOT\", \"CASSELTON\")) rcbd1$infoDesign #> $blocks #> [1] 5 #> #> $number.of.treatments #> [1] 20 #> #> $treatments #> [1] \"A\" \"B\" \"C\" \"D\" \"E\" \"F\" \"G\" \"H\" \"I\" \"J\" \"K\" \"L\" \"M\" \"N\" \"O\" \"P\" \"Q\" \"R\" \"S\" #> [20] \"T\" #> #> $locations #> [1] 3 #> #> $plotNumber #> [1] 101 201 301 401 501 1001 1101 1201 1301 1401 2001 2101 2201 2301 2401 #> #> $locationNames #> [1] \"FARGO\" \"MINOT\" \"CASSELTON\" #> #> $seed #> [1] 1020 #> #> $id_design #> [1] 2 #> rcbd1$layoutRandom #> $Loc_FARGO #> Block --Treatments-- #> [1,] \"1\" \"P R L T E A J O M C K F I Q G D S H N B\" #> [2,] \"2\" \"Q H G M F D L P E B J N A I K C T R O S\" #> [3,] \"3\" \"R B G K H E S C F D I T P N Q M A O J L\" #> [4,] \"4\" \"M I T B N G O J Q C A L P E S R D K H F\" #> [5,] \"5\" \"M C Q O E H I A P S R L J G F B T D K N\" #> #> $Loc_MINOT #> Block --Treatments-- #> [1,] \"1\" \"F O C A G D L B I S P T H K M E N R Q J\" #> [2,] \"2\" \"Q H K A G D E M N O C S J I T L P F B R\" #> [3,] \"3\" \"B K D L O E A R F S I P G T C Q J N M H\" #> [4,] \"4\" \"C P L O B K E H Q G N A T R J F S M D I\" #> [5,] \"5\" \"G S D B H L Q K A P E J T R I C O F M N\" #> #> $Loc_CASSELTON #> Block --Treatments-- #> [1,] \"1\" \"P G T E L O K H D N S C M I A J Q R B F\" #> [2,] \"2\" \"C D L F A T I G S O B J M E R P H N Q K\" #> [3,] \"3\" \"C G K N B A L Q I F D H J M O P S T E R\" #> [4,] \"4\" \"E L H D F J A T S N B G Q M I O P C K R\" #> [5,] \"5\" \"T I M A H K E C Q L D J R B G S N O F P\" #> rcbd1$plotNumber #> $Loc_FARGO #> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14] #> [1,] 101 102 103 104 105 106 107 108 109 110 111 112 113 114 #> [2,] 140 139 138 137 136 135 134 133 132 131 130 129 128 127 #> [3,] 141 142 143 144 145 146 147 148 149 150 151 152 153 154 #> [4,] 180 179 178 177 176 175 174 173 172 171 170 169 168 167 #> [5,] 181 182 183 184 185 186 187 188 189 190 191 192 193 194 #> [,15] [,16] [,17] [,18] [,19] [,20] #> [1,] 115 116 117 118 119 120 #> [2,] 126 125 124 123 122 121 #> [3,] 155 156 157 158 159 160 #> [4,] 166 165 164 163 162 161 #> [5,] 195 196 197 198 199 200 #> #> $Loc_MINOT #> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14] #> [1,] 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 #> [2,] 1040 1039 1038 1037 1036 1035 1034 1033 1032 1031 1030 1029 1028 1027 #> [3,] 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 #> [4,] 1080 1079 1078 1077 1076 1075 1074 1073 1072 1071 1070 1069 1068 1067 #> [5,] 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 #> [,15] [,16] [,17] [,18] [,19] [,20] #> [1,] 1015 1016 1017 1018 1019 1020 #> [2,] 1026 1025 1024 1023 1022 1021 #> [3,] 1055 1056 1057 1058 1059 1060 #> [4,] 1066 1065 1064 1063 1062 1061 #> [5,] 1095 1096 1097 1098 1099 1100 #> #> $Loc_CASSELTON #> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14] #> [1,] 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 #> [2,] 2040 2039 2038 2037 2036 2035 2034 2033 2032 2031 2030 2029 2028 2027 #> [3,] 2041 2042 2043 2044 2045 2046 2047 2048 2049 2050 2051 2052 2053 2054 #> [4,] 2080 2079 2078 2077 2076 2075 2074 2073 2072 2071 2070 2069 2068 2067 #> [5,] 2081 2082 2083 2084 2085 2086 2087 2088 2089 2090 2091 2092 2093 2094 #> [,15] [,16] [,17] [,18] [,19] [,20] #> [1,] 2015 2016 2017 2018 2019 2020 #> [2,] 2026 2025 2024 2023 2022 2021 #> [3,] 2055 2056 2057 2058 2059 2060 #> [4,] 2066 2065 2064 2063 2062 2061 #> [5,] 2095 2096 2097 2098 2099 2100 #> head(rcbd1$fieldBook) #> ID LOCATION PLOT REP TREATMENT #> 1 1 FARGO 101 1 P #> 2 2 FARGO 102 1 R #> 3 3 FARGO 103 1 L #> 4 4 FARGO 104 1 T #> 5 5 FARGO 105 1 E #> 6 6 FARGO 106 1 A # Example 2: Generates a RCBD design with 6 blocks and 18 treatments in one location. # In this case, we show how to use the option data. treatments <- paste(\"ND-\", 1:18, sep = \"\") treatment_list <- data.frame(list(TREATMENT = treatments)) head(treatment_list) #> TREATMENT #> 1 ND-1 #> 2 ND-2 #> 3 ND-3 #> 4 ND-4 #> 5 ND-5 #> 6 ND-6 rcbd2 <- RCBD(reps = 6, l = 1, plotNumber = 101, continuous = FALSE, planter = \"serpentine\", seed = 13, locationNames = \"IBAGUE\", data = treatment_list) rcbd2$infoDesign #> $blocks #> [1] 6 #> #> $number.of.treatments #> [1] 18 #> #> $treatments #> [1] \"ND-1\" \"ND-2\" \"ND-3\" \"ND-4\" \"ND-5\" \"ND-6\" \"ND-7\" \"ND-8\" \"ND-9\" #> [10] \"ND-10\" \"ND-11\" \"ND-12\" \"ND-13\" \"ND-14\" \"ND-15\" \"ND-16\" \"ND-17\" \"ND-18\" #> #> $locations #> [1] 1 #> #> $plotNumber #> [1] 101 201 301 401 501 601 #> #> $locationNames #> [1] \"IBAGUE\" #> #> $seed #> [1] 13 #> #> $id_design #> [1] 2 #> rcbd2$layoutRandom #> $Loc_IBAGUE #> Block #> [1,] \"1\" #> [2,] \"2\" #> [3,] \"3\" #> [4,] \"4\" #> [5,] \"5\" #> [6,] \"6\" #> --Treatments-- #> [1,] \"ND-3 ND-5 ND-10 ND-13 ND-6 ND-14 ND-4 ND-8 ND-18 ND-1 ND-11 ND-2 ND-17 ND-12 ND-9 ND-7 ND-16 ND-15\" #> [2,] \"ND-15 ND-17 ND-12 ND-1 ND-11 ND-4 ND-8 ND-7 ND-5 ND-3 ND-14 ND-9 ND-10 ND-13 ND-2 ND-6 ND-18 ND-16\" #> [3,] \"ND-17 ND-12 ND-8 ND-14 ND-10 ND-6 ND-7 ND-18 ND-2 ND-1 ND-13 ND-9 ND-11 ND-15 ND-16 ND-3 ND-4 ND-5\" #> [4,] \"ND-14 ND-13 ND-16 ND-1 ND-8 ND-9 ND-15 ND-6 ND-7 ND-12 ND-10 ND-18 ND-11 ND-4 ND-3 ND-5 ND-2 ND-17\" #> [5,] \"ND-14 ND-11 ND-9 ND-4 ND-1 ND-16 ND-3 ND-8 ND-5 ND-7 ND-10 ND-18 ND-12 ND-6 ND-2 ND-15 ND-13 ND-17\" #> [6,] \"ND-3 ND-5 ND-17 ND-9 ND-6 ND-18 ND-1 ND-14 ND-12 ND-8 ND-4 ND-11 ND-15 ND-2 ND-10 ND-16 ND-13 ND-7\" #> rcbd2$plotNumber #> $Loc_IBAGUE #> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14] #> [1,] 101 102 103 104 105 106 107 108 109 110 111 112 113 114 #> [2,] 218 217 216 215 214 213 212 211 210 209 208 207 206 205 #> [3,] 301 302 303 304 305 306 307 308 309 310 311 312 313 314 #> [4,] 418 417 416 415 414 413 412 411 410 409 408 407 406 405 #> [5,] 501 502 503 504 505 506 507 508 509 510 511 512 513 514 #> [6,] 618 617 616 615 614 613 612 611 610 609 608 607 606 605 #> [,15] [,16] [,17] [,18] #> [1,] 115 116 117 118 #> [2,] 204 203 202 201 #> [3,] 315 316 317 318 #> [4,] 404 403 402 401 #> [5,] 515 516 517 518 #> [6,] 604 603 602 601 #> head(rcbd2$fieldBook) #> ID LOCATION PLOT REP TREATMENT #> 1 1 IBAGUE 101 1 ND-3 #> 2 2 IBAGUE 102 1 ND-5 #> 3 3 IBAGUE 103 1 ND-10 #> 4 4 IBAGUE 104 1 ND-13 #> 5 5 IBAGUE 105 1 ND-6 #> 6 6 IBAGUE 106 1 ND-14"},{"path":"https://didiermurillof.github.io/FielDHub/reference/RCBD_augmented.html","id":null,"dir":"Reference","previous_headings":"","what":"Generates an Augmented Randomized Complete Block Design (ARCBD) β€” RCBD_augmented","title":"Generates an Augmented Randomized Complete Block Design (ARCBD) β€” RCBD_augmented","text":"randomly generates augmented randomized complete block design across locations (ARCBD).","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/RCBD_augmented.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generates an Augmented Randomized Complete Block Design (ARCBD) β€” RCBD_augmented","text":"","code":"RCBD_augmented( lines = NULL, checks = NULL, b = NULL, l = 1, planter = \"serpentine\", plotNumber = 101, exptName = NULL, seed = NULL, locationNames = NULL, repsExpt = 1, random = TRUE, data = NULL, nrows = NULL, ncols = NULL )"},{"path":"https://didiermurillof.github.io/FielDHub/reference/RCBD_augmented.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generates an Augmented Randomized Complete Block Design (ARCBD) β€” RCBD_augmented","text":"lines Treatments, number lines test. checks Number checks per augmented block. b Number augmented blocks. l Number locations. default l = 1. planter Option serpentine cartesian arrangement. default planter = 'serpentine'. plotNumber Numeric vector starting plot number location. default plotNumber = 101. exptName (optional) Name experiment. seed (optional) Real number specifies starting seed obtain reproducible designs. locationNames (optional) Name location. repsExpt (optional) Number reps experiment. default repsExpt = 1. random Logical value randomize treatments . default random = TRUE. data (optional) Data frame labels treatments. nrows (optional) Number rows field. ncols (optional) Number columns field.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/RCBD_augmented.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generates an Augmented Randomized Complete Block Design (ARCBD) β€” RCBD_augmented","text":"list five elements. infoDesign list information design parameters. layoutRandom ARCBD layout randomization first location. plotNumber plot number layout first location. exptNames experiment names layout. data_entry data frame data input. fieldBook data frame ARCBD field book.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/RCBD_augmented.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Generates an Augmented Randomized Complete Block Design (ARCBD) β€” RCBD_augmented","text":"Federer, W. T. (1955). Experimental Design. Theory Application. New York, USA. Macmillan Company.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/RCBD_augmented.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Generates an Augmented Randomized Complete Block Design (ARCBD) β€” RCBD_augmented","text":"Didier Murillo [aut], Salvador Gezan [aut], Ana Heilman [ctb], Thomas Walk [ctb], Johan Aparicio [ctb], Richard Horsley [ctb]","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/RCBD_augmented.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generates an Augmented Randomized Complete Block Design (ARCBD) β€” RCBD_augmented","text":"","code":"# Example 1: Generates an ARCBD with 6 blocks, 3 checks for each, and 50 treatments # in two locations. ARCBD1 <- RCBD_augmented(lines = 50, checks = 3, b = 6, l = 2, planter = \"cartesian\", plotNumber = c(1,1001), seed = 23, locationNames = c(\"FARGO\", \"MINOT\")) ARCBD1$infoDesign #> $rows #> [1] 6 #> #> $columns #> [1] 12 #> #> $rows_within_blocks #> [1] 1 #> #> $columns_within_blocks #> [1] 12 #> #> $treatments #> [1] 50 #> #> $checks #> [1] 3 #> #> $blocks #> [1] 6 #> #> $plots_per_block #> [1] 12 12 12 12 12 8 #> #> $locations #> [1] 2 #> #> $fillers #> [1] 4 #> #> $seed #> [1] 23 #> #> $id_design #> [1] 14 #> ARCBD1$layoutRandom #> Col1 Col2 Col3 Col4 Col5 Col6 Col7 Col8 Col9 Col10 Col11 Col12 #> Row6 2 15 38 3 21 36 26 1 0 0 0 0 #> Row5 3 1 24 46 11 2 48 37 32 31 20 42 #> Row4 34 25 16 41 9 50 2 43 39 1 13 3 #> Row3 18 28 5 2 40 8 30 17 53 10 3 1 #> Row2 7 29 12 2 3 33 22 23 4 47 19 1 #> Row1 49 14 27 3 2 45 6 35 52 44 51 1 ARCBD1$exptNames #> V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 #> 1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 #> 2 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 #> 3 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 #> 4 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 #> 5 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 #> 6 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 ARCBD1$plotNumber #> V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 #> [1,] 61 62 63 64 65 66 67 68 0 0 0 0 #> [2,] 49 50 51 52 53 54 55 56 57 58 59 60 #> [3,] 37 38 39 40 41 42 43 44 45 46 47 48 #> [4,] 25 26 27 28 29 30 31 32 33 34 35 36 #> [5,] 13 14 15 16 17 18 19 20 21 22 23 24 #> [6,] 1 2 3 4 5 6 7 8 9 10 11 12 head(ARCBD1$fieldBook, 12) #> ID EXPT LOCATION YEAR PLOT ROW COLUMN CHECKS BLOCK ENTRY TREATMENT #> 1 1 Expt1 FARGO 2024 1 1 1 0 1 49 G49 #> 2 2 Expt1 FARGO 2024 2 1 2 0 1 14 G14 #> 3 3 Expt1 FARGO 2024 3 1 3 0 1 27 G27 #> 4 4 Expt1 FARGO 2024 4 1 4 1 1 3 CH3 #> 5 5 Expt1 FARGO 2024 5 1 5 1 1 2 CH2 #> 6 6 Expt1 FARGO 2024 6 1 6 0 1 45 G45 #> 7 7 Expt1 FARGO 2024 7 1 7 0 1 6 G6 #> 8 8 Expt1 FARGO 2024 8 1 8 0 1 35 G35 #> 9 9 Expt1 FARGO 2024 9 1 9 0 1 52 G52 #> 10 10 Expt1 FARGO 2024 10 1 10 0 1 44 G44 #> 11 11 Expt1 FARGO 2024 11 1 11 0 1 51 G51 #> 12 12 Expt1 FARGO 2024 12 1 12 1 1 1 CH1 # Example 2: Generates an ARCBD with 17 blocks, 4 checks for each, and 350 treatments # in 3 locations. # In this case, we show how to use the option data. checks <- 4; list_checks <- paste(\"CH\", 1:checks, sep = \"\") treatments <- paste(\"G\", 5:354, sep = \"\") treatment_list <- data.frame(list(ENTRY = 1:354, NAME = c(list_checks, treatments))) head(treatment_list, 12) #> ENTRY NAME #> 1 1 CH1 #> 2 2 CH2 #> 3 3 CH3 #> 4 4 CH4 #> 5 5 G5 #> 6 6 G6 #> 7 7 G7 #> 8 8 G8 #> 9 9 G9 #> 10 10 G10 #> 11 11 G11 #> 12 12 G12 ARCBD2 <- RCBD_augmented(lines = 350, checks = 4, b = 17, l = 3, planter = \"serpentine\", plotNumber = c(101,1001,2001), seed = 24, locationNames = LETTERS[1:3], data = treatment_list) ARCBD2$infoDesign #> $rows #> [1] 17 #> #> $columns #> [1] 25 #> #> $rows_within_blocks #> [1] 1 #> #> $columns_within_blocks #> [1] 25 #> #> $treatments #> [1] 350 #> #> $checks #> [1] 4 #> #> $blocks #> [1] 17 #> #> $plots_per_block #> [1] 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 18 #> #> $locations #> [1] 3 #> #> $fillers #> [1] 7 #> #> $seed #> [1] 24 #> #> $id_design #> [1] 14 #> ARCBD2$layoutRandom #> Col1 Col2 Col3 Col4 Col5 Col6 Col7 Col8 Col9 Col10 Col11 Col12 Col13 #> Row17 257 259 1 198 331 66 3 238 170 176 126 207 225 #> Row16 17 12 1 314 22 235 77 340 188 76 101 2 16 #> Row15 229 231 54 3 305 4 128 50 30 55 1 337 24 #> Row14 63 45 62 40 140 322 82 228 283 142 53 211 7 #> Row13 253 2 68 113 13 279 47 57 4 132 3 167 159 #> Row12 282 205 192 324 315 2 247 124 179 58 105 273 31 #> Row11 110 125 85 332 250 248 265 255 2 251 52 42 236 #> Row10 173 154 338 327 78 3 96 177 193 4 244 191 348 #> Row9 4 2 25 103 36 155 260 246 189 49 197 284 242 #> Row8 107 321 186 4 163 33 71 109 100 174 309 18 135 #> Row7 2 239 252 213 261 150 3 266 277 307 4 95 311 #> Row6 133 75 153 102 274 2 4 1 270 285 3 240 276 #> Row5 234 56 349 288 202 300 79 87 157 64 168 1 4 #> Row4 160 195 2 289 161 83 143 271 141 144 94 320 3 #> Row3 268 209 4 185 308 115 81 342 249 258 120 1 2 #> Row2 172 347 346 215 298 86 1 116 328 224 139 3 4 #> Row1 345 130 4 162 1 123 2 39 9 302 210 352 138 #> Col14 Col15 Col16 Col17 Col18 Col19 Col20 Col21 Col22 Col23 Col24 Col25 #> Row17 122 4 208 187 2 0 0 0 0 0 0 0 #> Row16 3 219 111 291 316 4 341 169 222 237 65 281 #> Row15 329 263 2 74 108 318 350 147 306 325 37 43 #> Row14 136 310 5 2 199 1 4 164 46 3 158 223 #> Row13 23 1 148 117 201 28 11 119 190 73 72 99 #> Row12 32 1 27 243 241 21 3 303 4 106 127 254 #> Row11 35 1 216 61 3 4 230 69 245 339 98 14 #> Row10 1 227 323 2 203 118 181 88 104 10 272 175 #> Row9 335 217 319 200 3 152 97 267 44 275 92 1 #> Row8 221 333 121 2 1 3 214 226 183 15 194 351 #> Row7 313 60 293 38 59 67 232 134 1 178 93 114 #> Row6 156 41 165 146 51 317 292 280 343 171 334 84 #> Row5 220 34 131 262 3 180 129 145 2 212 91 278 #> Row4 19 353 301 6 4 206 304 1 233 354 166 20 #> Row3 294 89 269 29 26 286 290 336 80 3 149 312 #> Row2 48 295 151 287 2 326 70 264 204 137 296 8 #> Row1 297 330 3 256 90 184 196 218 344 299 182 112 ARCBD2$exptNames #> V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 #> 1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 #> 2 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 #> 3 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 #> 4 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 #> 5 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 #> 6 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 #> 7 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 #> 8 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 #> 9 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 #> 10 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 #> 11 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 #> 12 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 #> 13 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 #> 14 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 #> 15 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 #> 16 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 #> 17 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 #> V13 V14 V15 V16 V17 V18 V19 V20 V21 V22 V23 V24 #> 1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 #> 2 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 #> 3 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 #> 4 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 #> 5 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 #> 6 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 #> 7 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 #> 8 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 #> 9 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 #> 10 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 #> 11 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 #> 12 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 #> 13 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 #> 14 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 #> 15 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 #> 16 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 #> 17 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 #> V25 #> 1 Expt1 #> 2 Expt1 #> 3 Expt1 #> 4 Expt1 #> 5 Expt1 #> 6 Expt1 #> 7 Expt1 #> 8 Expt1 #> 9 Expt1 #> 10 Expt1 #> 11 Expt1 #> 12 Expt1 #> 13 Expt1 #> 14 Expt1 #> 15 Expt1 #> 16 Expt1 #> 17 Expt1 ARCBD2$plotNumber #> V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 V13 V14 V15 V16 V17 V18 #> [1,] 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 #> [2,] 500 499 498 497 496 495 494 493 492 491 490 489 488 487 486 485 484 483 #> [3,] 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 #> [4,] 450 449 448 447 446 445 444 443 442 441 440 439 438 437 436 435 434 433 #> [5,] 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 #> [6,] 400 399 398 397 396 395 394 393 392 391 390 389 388 387 386 385 384 383 #> [7,] 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 #> [8,] 350 349 348 347 346 345 344 343 342 341 340 339 338 337 336 335 334 333 #> [9,] 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 #> [10,] 300 299 298 297 296 295 294 293 292 291 290 289 288 287 286 285 284 283 #> [11,] 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 #> [12,] 250 249 248 247 246 245 244 243 242 241 240 239 238 237 236 235 234 233 #> [13,] 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 #> [14,] 200 199 198 197 196 195 194 193 192 191 190 189 188 187 186 185 184 183 #> [15,] 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 #> [16,] 150 149 148 147 146 145 144 143 142 141 140 139 138 137 136 135 134 133 #> [17,] 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 #> V19 V20 V21 V22 V23 V24 V25 #> [1,] 0 0 0 0 0 0 0 #> [2,] 482 481 480 479 478 477 476 #> [3,] 469 470 471 472 473 474 475 #> [4,] 432 431 430 429 428 427 426 #> [5,] 419 420 421 422 423 424 425 #> [6,] 382 381 380 379 378 377 376 #> [7,] 369 370 371 372 373 374 375 #> [8,] 332 331 330 329 328 327 326 #> [9,] 319 320 321 322 323 324 325 #> [10,] 282 281 280 279 278 277 276 #> [11,] 269 270 271 272 273 274 275 #> [12,] 232 231 230 229 228 227 226 #> [13,] 219 220 221 222 223 224 225 #> [14,] 182 181 180 179 178 177 176 #> [15,] 169 170 171 172 173 174 175 #> [16,] 132 131 130 129 128 127 126 #> [17,] 119 120 121 122 123 124 125 head(ARCBD2$fieldBook, 12) #> ID EXPT LOCATION YEAR PLOT ROW COLUMN CHECKS BLOCK ENTRY TREATMENT #> 1 1 Expt1 A 2024 101 1 1 0 1 345 G345 #> 2 2 Expt1 A 2024 102 1 2 0 1 130 G130 #> 3 3 Expt1 A 2024 103 1 3 1 1 4 CH4 #> 4 4 Expt1 A 2024 104 1 4 0 1 162 G162 #> 5 5 Expt1 A 2024 105 1 5 1 1 1 CH1 #> 6 6 Expt1 A 2024 106 1 6 0 1 123 G123 #> 7 7 Expt1 A 2024 107 1 7 1 1 2 CH2 #> 8 8 Expt1 A 2024 108 1 8 0 1 39 G39 #> 9 9 Expt1 A 2024 109 1 9 0 1 9 G9 #> 10 10 Expt1 A 2024 110 1 10 0 1 302 G302 #> 11 11 Expt1 A 2024 111 1 11 0 1 210 G210 #> 12 12 Expt1 A 2024 112 1 12 0 1 352 G352"},{"path":"https://didiermurillof.github.io/FielDHub/reference/alpha_lattice.html","id":null,"dir":"Reference","previous_headings":"","what":"Generates an Alpha Design β€” alpha_lattice","title":"Generates an Alpha Design β€” alpha_lattice","text":"Randomly generates alpha design like alpha(0,1) across multiple locations.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/alpha_lattice.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generates an Alpha Design β€” alpha_lattice","text":"","code":"alpha_lattice( t = NULL, k = NULL, r = NULL, l = 1, plotNumber = 101, locationNames = NULL, seed = NULL, data = NULL )"},{"path":"https://didiermurillof.github.io/FielDHub/reference/alpha_lattice.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generates an Alpha Design β€” alpha_lattice","text":"t Number treatments. k Size incomplete blocks (number units per incomplete block). r Number full blocks (resolvable replicates) (also number replicates per treatment). l Number locations. default l = 1. plotNumber Numeric vector starting plot number location. default plotNumber = 101. locationNames (optional) String names l locations. seed (optional) Real number specifies starting seed obtain reproducible designs. data (optional) Data frame label list treatments.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/alpha_lattice.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generates an Alpha Design β€” alpha_lattice","text":"list two elements. infoDesign list information design parameters. fieldBook data frame alpha design field book.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/alpha_lattice.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Generates an Alpha Design β€” alpha_lattice","text":"Edmondson., R. N. (2021). blocksdesign: Nested crossed block designs factorial unstructured treatment sets. https://CRAN.R-project.org/package=blocksdesign","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/alpha_lattice.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Generates an Alpha Design β€” alpha_lattice","text":"Didier Murillo [aut], Salvador Gezan [aut], Ana Heilman [ctb], Thomas Walk [ctb], Johan Aparicio [ctb], Richard Horsley [ctb]","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/alpha_lattice.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generates an Alpha Design β€” alpha_lattice","text":"","code":"# Example 1: Generates an alpha design with 4 full blocks and 15 treatments. # Size of IBlocks k = 3. alphalattice1 <- alpha_lattice(t = 15, k = 3, r = 4, l = 1, plotNumber = 101, locationNames = \"GreenHouse\", seed = 1247) alphalattice1$infoDesign #> $Reps #> [1] 4 #> #> $iBlocks #> [1] 5 #> #> $NumberTreatments #> [1] 15 #> #> $NumberLocations #> [1] 1 #> #> $Locations #> [1] \"GREENHOUSE\" #> #> $seed #> [1] 1247 #> #> $lambda #> [1] 0.5714286 #> #> $id_design #> [1] 12 #> head(alphalattice1$fieldBook, 10) #> ID LOCATION PLOT REP IBLOCK UNIT ENTRY TREATMENT #> 1 1 GREENHOUSE 101 1 1 1 8 G-8 #> 2 2 GREENHOUSE 102 1 1 2 3 G-3 #> 3 3 GREENHOUSE 103 1 1 3 2 G-2 #> 4 4 GREENHOUSE 104 1 2 1 6 G-6 #> 5 5 GREENHOUSE 105 1 2 2 9 G-9 #> 6 6 GREENHOUSE 106 1 2 3 12 G-12 #> 7 7 GREENHOUSE 107 1 3 1 14 G-14 #> 8 8 GREENHOUSE 108 1 3 2 1 G-1 #> 9 9 GREENHOUSE 109 1 3 3 5 G-5 #> 10 10 GREENHOUSE 110 1 4 1 15 G-15 # Example 2: Generates an alpha design with 3 full blocks and 25 treatment. # Size of IBlocks k = 5. # In this case, we show how to use the option data. treatments <- paste(\"G-\", 1:25, sep = \"\") ENTRY <- 1:25 treatment_list <- data.frame(list(ENTRY = ENTRY, TREATMENT = treatments)) head(treatment_list) #> ENTRY TREATMENT #> 1 1 G-1 #> 2 2 G-2 #> 3 3 G-3 #> 4 4 G-4 #> 5 5 G-5 #> 6 6 G-6 alphalattice2 <- alpha_lattice(t = 25, k = 5, r = 3, l = 1, plotNumber = 1001, locationNames = \"A\", seed = 1945, data = treatment_list) alphalattice2$infoDesign #> $Reps #> [1] 3 #> #> $iBlocks #> [1] 5 #> #> $NumberTreatments #> [1] 25 #> #> $NumberLocations #> [1] 1 #> #> $Locations #> [1] \"A\" #> #> $seed #> [1] 1945 #> #> $lambda #> [1] 0.5 #> #> $id_design #> [1] 12 #> head(alphalattice2$fieldBook, 10) #> ID LOCATION PLOT REP IBLOCK UNIT ENTRY TREATMENT #> 1 1 A 1001 1 1 1 20 G-20 #> 2 2 A 1002 1 1 2 5 G-5 #> 3 3 A 1003 1 1 3 10 G-10 #> 4 4 A 1004 1 1 4 1 G-1 #> 5 5 A 1005 1 1 5 12 G-12 #> 6 6 A 1006 1 2 1 19 G-19 #> 7 7 A 1007 1 2 2 8 G-8 #> 8 8 A 1008 1 2 3 13 G-13 #> 9 9 A 1009 1 2 4 9 G-9 #> 10 10 A 1010 1 2 5 17 G-17"},{"path":"https://didiermurillof.github.io/FielDHub/reference/diagonal_arrangement.html","id":null,"dir":"Reference","previous_headings":"","what":"Spatial Un-replicated Diagonal Arrangement Design β€” diagonal_arrangement","title":"Spatial Un-replicated Diagonal Arrangement Design β€” diagonal_arrangement","text":"Randomly generates spatial un-replicated diagonal arrangement design.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/diagonal_arrangement.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Spatial Un-replicated Diagonal Arrangement Design β€” diagonal_arrangement","text":"","code":"diagonal_arrangement( nrows = NULL, ncols = NULL, lines = NULL, checks = NULL, planter = \"serpentine\", l = 1, plotNumber = 101, kindExpt = \"SUDC\", splitBy = \"row\", seed = NULL, blocks = NULL, exptName = NULL, locationNames = NULL, multiLocationData = FALSE, data = NULL )"},{"path":"https://didiermurillof.github.io/FielDHub/reference/diagonal_arrangement.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Spatial Un-replicated Diagonal Arrangement Design β€” diagonal_arrangement","text":"nrows Number rows field. ncols Number columns field. lines Number genotypes, experimental lines treatments. checks Number genotypes checks. planter Option serpentine cartesian plot arrangement. default planter = 'serpentine'. l Number locations sites. default l = 1. plotNumber Numeric vector starting plot number location. default plotNumber = 101. kindExpt Type diagonal design, single options: Single Un-replicated Diagonal Checks 'SUDC' Decision Blocks Un-replicated Design Diagonal Checks 'DBUDC' multiple experiments. default kindExpt = 'SUDC'. splitBy Option split field kindExpt = 'DBUDC' selected. default splitBy = 'row'. seed (optional) Real number specifies starting seed obtain reproducible designs. blocks Number experiments blocks generate DBUDC design. kindExpt = 'DBUDC' data null, blocks mandatory. exptName (optional) Name experiment. locationNames (optional) Names location. multiLocationData (optional) Option pass entry list multiple locations. default multiLocationData = FALSE. data (optional) Data frame 2 columns: ENTRY | NAME .","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/diagonal_arrangement.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Spatial Un-replicated Diagonal Arrangement Design β€” diagonal_arrangement","text":"list five elements. infoDesign list information design parameters. layoutRandom matrix randomization layout. plotsNumber matrix layout plot number. data_entry data frame data input. fieldBook data frame field book design. includes index (Row, Column).","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/diagonal_arrangement.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Spatial Un-replicated Diagonal Arrangement Design β€” diagonal_arrangement","text":"Clarke, G. P. Y., & Stefanova, K. T. (2011). Optimal design early-generation plant breeding trials unreplicated partially replicated test lines. Australian & New Zealand Journal Statistics, 53(4), 461–480.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/diagonal_arrangement.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Spatial Un-replicated Diagonal Arrangement Design β€” diagonal_arrangement","text":"Didier Murillo [aut], Salvador Gezan [aut], Ana Heilman [ctb], Thomas Walk [ctb], Johan Aparicio [ctb], Richard Horsley [ctb]","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/diagonal_arrangement.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Spatial Un-replicated Diagonal Arrangement Design β€” diagonal_arrangement","text":"","code":"# Example 1: Generates a spatial single diagonal arrangement design in one location # with 270 treatments and 30 check plots for a field with dimensions 15 rows x 20 cols # in a serpentine arrangement. spatd <- diagonal_arrangement( nrows = 15, ncols = 20, lines = 270, checks = 4, plotNumber = 101, kindExpt = \"SUDC\", planter = \"serpentine\", seed = 1987, exptName = \"20WRY1\", locationNames = \"MINOT\" ) spatd$infoDesign #> $rows #> [1] 15 #> #> $columns #> [1] 20 #> #> $treatments #> [1] 270 #> #> $checks #> [1] 4 #> #> $entry_checks #> $entry_checks[[1]] #> [1] 1 2 3 4 #> #> #> $rep_checks #> $rep_checks[[1]] #> [1] 8 7 8 7 #> #> #> $locations #> [1] 1 #> #> $planter #> [1] \"serpentine\" #> #> $percent_checks #> [1] \"10%\" #> #> $fillers #> [1] 0 #> #> $seed #> [1] 1987 #> #> $id_design #> [1] 15 #> spatd$layoutRandom #> [[1]] #> Col1 Col2 Col3 Col4 Col5 Col6 Col7 Col8 Col9 Col10 Col11 Col12 Col13 #> Row15 164 3 153 11 221 179 151 139 58 22 266 2 129 #> Row14 89 182 185 38 1 253 156 241 160 252 214 86 130 #> Row13 15 148 82 213 44 194 269 2 265 169 48 245 210 #> Row12 1 124 52 177 5 261 47 40 17 87 3 104 147 #> Row11 100 127 136 4 19 65 158 46 18 229 157 274 59 #> Row10 94 50 27 31 220 166 3 172 170 12 16 176 137 #> Row9 205 212 115 142 110 208 224 216 222 2 246 42 251 #> Row8 175 92 1 197 243 234 236 99 211 67 140 39 3 #> Row7 75 76 8 122 200 1 264 25 138 199 107 120 131 #> Row6 132 93 254 7 247 60 45 171 3 117 103 116 190 #> Row5 181 2 70 79 85 133 203 134 184 273 34 1 174 #> Row4 71 204 159 29 2 83 26 64 119 145 240 223 225 #> Row3 144 231 80 255 43 187 112 4 168 98 32 41 96 #> Row2 4 196 238 235 97 183 111 143 186 237 2 232 263 #> Row1 55 108 248 4 250 217 123 249 126 28 23 118 20 #> Col14 Col15 Col16 Col17 Col18 Col19 Col20 #> Row15 33 109 154 88 30 53 95 #> Row14 163 4 219 68 270 173 90 #> Row13 244 125 149 226 1 54 56 #> Row12 259 233 267 201 193 6 10 #> Row11 2 114 21 77 272 72 24 #> Row10 102 155 36 3 9 162 191 #> Row9 218 106 228 258 167 84 1 #> Row8 230 192 62 135 198 14 69 #> Row7 161 81 3 165 189 268 57 #> Row6 128 146 206 141 215 4 195 #> Row5 61 202 51 242 73 63 207 #> Row4 113 1 78 178 152 37 180 #> Row3 101 74 66 239 4 105 256 #> Row2 49 262 91 257 121 260 209 #> Row1 3 13 150 188 35 227 271 #> spatd$plotsNumber #> [[1]] #> Col1 Col2 Col3 Col4 Col5 Col6 Col7 Col8 Col9 Col10 Col11 Col12 Col13 #> Row15 381 382 383 384 385 386 387 388 389 390 391 392 393 #> Row14 380 379 378 377 376 375 374 373 372 371 370 369 368 #> Row13 341 342 343 344 345 346 347 348 349 350 351 352 353 #> Row12 340 339 338 337 336 335 334 333 332 331 330 329 328 #> Row11 301 302 303 304 305 306 307 308 309 310 311 312 313 #> Row10 300 299 298 297 296 295 294 293 292 291 290 289 288 #> Row9 261 262 263 264 265 266 267 268 269 270 271 272 273 #> Row8 260 259 258 257 256 255 254 253 252 251 250 249 248 #> Row7 221 222 223 224 225 226 227 228 229 230 231 232 233 #> Row6 220 219 218 217 216 215 214 213 212 211 210 209 208 #> Row5 181 182 183 184 185 186 187 188 189 190 191 192 193 #> Row4 180 179 178 177 176 175 174 173 172 171 170 169 168 #> Row3 141 142 143 144 145 146 147 148 149 150 151 152 153 #> Row2 140 139 138 137 136 135 134 133 132 131 130 129 128 #> Row1 101 102 103 104 105 106 107 108 109 110 111 112 113 #> Col14 Col15 Col16 Col17 Col18 Col19 Col20 #> Row15 394 395 396 397 398 399 400 #> Row14 367 366 365 364 363 362 361 #> Row13 354 355 356 357 358 359 360 #> Row12 327 326 325 324 323 322 321 #> Row11 314 315 316 317 318 319 320 #> Row10 287 286 285 284 283 282 281 #> Row9 274 275 276 277 278 279 280 #> Row8 247 246 245 244 243 242 241 #> Row7 234 235 236 237 238 239 240 #> Row6 207 206 205 204 203 202 201 #> Row5 194 195 196 197 198 199 200 #> Row4 167 166 165 164 163 162 161 #> Row3 154 155 156 157 158 159 160 #> Row2 127 126 125 124 123 122 121 #> Row1 114 115 116 117 118 119 120 #> head(spatd$fieldBook, 12) #> ID EXPT LOCATION YEAR PLOT ROW COLUMN CHECKS ENTRY TREATMENT #> 1 1 20WRY1 MINOT 2024 101 1 1 0 55 Gen-55 #> 2 2 20WRY1 MINOT 2024 102 1 2 0 108 Gen-108 #> 3 3 20WRY1 MINOT 2024 103 1 3 0 248 Gen-248 #> 4 4 20WRY1 MINOT 2024 104 1 4 4 4 Check-4 #> 5 5 20WRY1 MINOT 2024 105 1 5 0 250 Gen-250 #> 6 6 20WRY1 MINOT 2024 106 1 6 0 217 Gen-217 #> 7 7 20WRY1 MINOT 2024 107 1 7 0 123 Gen-123 #> 8 8 20WRY1 MINOT 2024 108 1 8 0 249 Gen-249 #> 9 9 20WRY1 MINOT 2024 109 1 9 0 126 Gen-126 #> 10 10 20WRY1 MINOT 2024 110 1 10 0 28 Gen-28 #> 11 11 20WRY1 MINOT 2024 111 1 11 0 23 Gen-23 #> 12 12 20WRY1 MINOT 2024 112 1 12 0 118 Gen-118 # Example 2: Generates a spatial decision block diagonal arrangement design in one location # with 720 treatments allocated in 5 experiments or blocks for a field with dimensions # 30 rows x 26 cols in a serpentine arrangement. In this case, we show how to set up the data # option with the entries list. checks <- 5;expts <- 5 list_checks <- paste(\"CH\", 1:checks, sep = \"\") treatments <- paste(\"G\", 6:725, sep = \"\") treatment_list <- data.frame(list(ENTRY = 1:725, NAME = c(list_checks, treatments))) head(treatment_list, 12) #> ENTRY NAME #> 1 1 CH1 #> 2 2 CH2 #> 3 3 CH3 #> 4 4 CH4 #> 5 5 CH5 #> 6 6 G6 #> 7 7 G7 #> 8 8 G8 #> 9 9 G9 #> 10 10 G10 #> 11 11 G11 #> 12 12 G12 tail(treatment_list, 12) #> ENTRY NAME #> 714 714 G714 #> 715 715 G715 #> 716 716 G716 #> 717 717 G717 #> 718 718 G718 #> 719 719 G719 #> 720 720 G720 #> 721 721 G721 #> 722 722 G722 #> 723 723 G723 #> 724 724 G724 #> 725 725 G725 spatDB <- diagonal_arrangement( nrows = 30, ncols = 26, checks = 5, plotNumber = 1, kindExpt = \"DBUDC\", planter = \"serpentine\", splitBy = \"row\", blocks = c(150,155,95,200,120), data = treatment_list ) spatDB$infoDesign #> $rows #> [1] 30 #> #> $columns #> [1] 26 #> #> $treatments #> [1] 150 155 95 200 120 #> #> $checks #> [1] 5 #> #> $entry_checks #> $entry_checks[[1]] #> [1] 1 2 3 4 5 #> #> #> $rep_checks #> $rep_checks[[1]] #> [1] 10 13 13 11 13 #> #> #> $locations #> [1] 1 #> #> $planter #> [1] \"serpentine\" #> #> $percent_checks #> [1] \"7.7%\" #> #> $fillers #> [1] 0 #> #> $seed #> [1] 24210 #> #> $id_design #> [1] 15 #> spatDB$layoutRandom #> [[1]] #> Col1 Col2 Col3 Col4 Col5 Col6 Col7 Col8 Col9 Col10 Col11 Col12 Col13 #> Row30 702 3 686 699 642 709 701 689 664 720 696 633 708 #> Row29 722 649 616 627 716 4 673 639 711 641 680 688 710 #> Row28 698 615 631 674 672 636 626 685 608 4 651 697 650 #> Row27 5 679 629 677 606 621 692 662 694 725 663 669 666 #> Row26 661 622 610 678 3 612 687 657 713 609 675 670 704 #> Row25 566 576 522 514 491 575 433 598 4 432 473 567 454 #> Row24 529 500 488 518 580 458 526 525 419 480 548 605 5 #> Row23 508 410 602 3 471 588 470 498 492 474 437 472 558 #> Row22 442 541 468 552 463 482 449 2 584 443 423 599 535 #> Row21 446 475 589 467 537 422 542 416 572 435 411 3 487 #> Row20 560 547 3 460 597 429 448 469 590 409 464 506 478 #> Row19 600 530 550 504 520 521 1 461 536 556 486 509 519 #> Row18 436 544 447 424 415 545 543 438 512 595 4 578 534 #> Row17 334 5 340 361 396 345 365 342 384 373 390 392 316 #> Row16 367 366 335 387 404 2 311 395 389 348 328 394 380 #> Row15 397 320 356 351 314 327 339 403 383 5 377 319 374 #> Row14 1 321 331 337 353 402 352 364 358 322 369 329 405 #> Row13 209 256 242 296 5 201 272 237 310 279 158 243 274 #> Row12 186 292 222 193 275 179 200 261 3 252 204 250 289 #> Row11 221 168 176 301 297 184 224 271 244 263 161 188 2 #> Row10 306 206 307 2 300 298 255 278 284 295 259 173 241 #> Row9 302 190 251 170 187 178 293 2 157 230 260 240 159 #> Row8 246 181 189 277 192 232 162 228 305 167 245 5 194 #> Row7 58 41 1 124 57 55 11 199 171 254 291 182 304 #> Row6 96 133 44 81 98 139 2 66 62 7 53 70 20 #> Row5 140 85 65 31 39 106 73 33 76 112 4 34 54 #> Row4 145 3 50 128 64 137 95 42 144 120 92 118 115 #> Row3 107 67 61 149 80 5 101 47 27 77 151 127 74 #> Row2 138 116 122 88 154 117 29 110 78 4 60 83 113 #> Row1 2 94 102 114 26 79 91 131 25 109 8 6 49 #> Col14 Col15 Col16 Col17 Col18 Col19 Col20 Col21 Col22 Col23 Col24 Col25 #> Row30 723 5 655 611 667 700 619 617 721 623 624 635 #> Row29 658 714 706 643 684 1 647 638 648 705 625 719 #> Row28 681 640 652 654 630 715 646 724 637 2 620 718 #> Row27 1 690 607 682 668 613 659 644 628 653 693 634 #> Row26 671 691 614 676 2 632 717 660 665 703 707 618 #> Row25 462 455 408 596 406 479 451 591 3 494 503 513 #> Row24 583 426 453 483 561 496 456 571 430 440 570 459 #> Row23 527 553 466 5 418 524 445 420 450 477 563 452 #> Row22 431 555 585 538 413 417 577 4 485 546 489 551 #> Row21 516 407 594 439 523 604 414 481 532 539 510 1 #> Row20 562 586 2 581 573 515 531 425 501 444 587 421 #> Row19 517 499 507 465 484 495 5 528 559 434 574 579 #> Row18 412 603 476 490 565 511 457 540 582 593 2 568 #> Row17 349 3 336 368 341 569 564 557 493 428 554 502 #> Row16 385 355 323 378 375 4 318 381 399 333 362 401 #> Row15 376 354 391 398 350 338 332 346 330 2 382 313 #> Row14 3 370 386 315 325 371 324 372 379 326 317 312 #> Row13 203 247 285 281 5 215 400 347 363 393 357 388 #> Row12 264 191 286 174 225 269 197 238 1 202 217 164 #> Row11 282 268 223 235 165 180 163 231 183 308 198 299 #> Row10 216 273 177 4 294 156 276 207 169 160 195 229 #> Row9 210 196 233 267 249 227 290 4 205 266 211 258 #> Row8 219 208 280 175 172 309 236 234 239 283 212 1 #> Row7 270 287 5 166 185 213 218 226 220 288 248 265 #> Row6 105 32 84 12 103 16 4 35 19 69 71 87 #> Row5 153 130 30 63 152 150 46 141 68 38 3 10 #> Row4 155 2 125 147 56 132 9 119 59 13 146 121 #> Row3 45 52 21 72 129 1 14 18 43 90 36 75 #> Row2 86 134 24 22 15 143 93 17 97 3 99 100 #> Row1 5 136 37 48 40 111 135 104 123 28 82 148 #> Col26 #> Row30 645 #> Row29 695 #> Row28 712 #> Row27 683 #> Row26 656 #> Row25 497 #> Row24 1 #> Row23 533 #> Row22 427 #> Row21 505 #> Row20 549 #> Row19 592 #> Row18 601 #> Row17 441 #> Row16 343 #> Row15 359 #> Row14 360 #> Row13 344 #> Row12 303 #> Row11 3 #> Row10 214 #> Row9 253 #> Row8 257 #> Row7 262 #> Row6 108 #> Row5 89 #> Row4 51 #> Row3 142 #> Row2 126 #> Row1 23 #> spatDB$plotsNumber #> [[1]] #> Col1 Col2 Col3 Col4 Col5 Col6 Col7 Col8 Col9 Col10 Col11 Col12 Col13 #> Row30 780 779 778 777 776 775 774 773 772 771 770 769 768 #> Row29 729 730 731 732 733 734 735 736 737 738 739 740 741 #> Row28 728 727 726 725 724 723 722 721 720 719 718 717 716 #> Row27 677 678 679 680 681 682 683 684 685 686 687 688 689 #> Row26 676 675 674 673 672 671 670 669 668 667 666 665 664 #> Row25 625 626 627 628 629 630 631 632 633 634 635 636 637 #> Row24 624 623 622 621 620 619 618 617 616 615 614 613 612 #> Row23 573 574 575 576 577 578 579 580 581 582 583 584 585 #> Row22 572 571 570 569 568 567 566 565 564 563 562 561 560 #> Row21 521 522 523 524 525 526 527 528 529 530 531 532 533 #> Row20 520 519 518 517 516 515 514 513 512 511 510 509 508 #> Row19 469 470 471 472 473 474 475 476 477 478 479 480 481 #> Row18 468 467 466 465 464 463 462 461 460 459 458 457 456 #> Row17 417 418 419 420 421 422 423 424 425 426 427 428 429 #> Row16 416 415 414 413 412 411 410 409 408 407 406 405 404 #> Row15 365 366 367 368 369 370 371 372 373 374 375 376 377 #> Row14 364 363 362 361 360 359 358 357 356 355 354 353 352 #> Row13 313 314 315 316 317 318 319 320 321 322 323 324 325 #> Row12 312 311 310 309 308 307 306 305 304 303 302 301 300 #> Row11 261 262 263 264 265 266 267 268 269 270 271 272 273 #> Row10 260 259 258 257 256 255 254 253 252 251 250 249 248 #> Row9 209 210 211 212 213 214 215 216 217 218 219 220 221 #> Row8 208 207 206 205 204 203 202 201 200 199 198 197 196 #> Row7 157 158 159 160 161 162 163 164 165 166 167 168 169 #> Row6 156 155 154 153 152 151 150 149 148 147 146 145 144 #> Row5 105 106 107 108 109 110 111 112 113 114 115 116 117 #> Row4 104 103 102 101 100 99 98 97 96 95 94 93 92 #> Row3 53 54 55 56 57 58 59 60 61 62 63 64 65 #> Row2 52 51 50 49 48 47 46 45 44 43 42 41 40 #> Row1 1 2 3 4 5 6 7 8 9 10 11 12 13 #> Col14 Col15 Col16 Col17 Col18 Col19 Col20 Col21 Col22 Col23 Col24 Col25 #> Row30 767 766 765 764 763 762 761 760 759 758 757 756 #> Row29 742 743 744 745 746 747 748 749 750 751 752 753 #> Row28 715 714 713 712 711 710 709 708 707 706 705 704 #> Row27 690 691 692 693 694 695 696 697 698 699 700 701 #> Row26 663 662 661 660 659 658 657 656 655 654 653 652 #> Row25 638 639 640 641 642 643 644 645 646 647 648 649 #> Row24 611 610 609 608 607 606 605 604 603 602 601 600 #> Row23 586 587 588 589 590 591 592 593 594 595 596 597 #> Row22 559 558 557 556 555 554 553 552 551 550 549 548 #> Row21 534 535 536 537 538 539 540 541 542 543 544 545 #> Row20 507 506 505 504 503 502 501 500 499 498 497 496 #> Row19 482 483 484 485 486 487 488 489 490 491 492 493 #> Row18 455 454 453 452 451 450 449 448 447 446 445 444 #> Row17 430 431 432 433 434 435 436 437 438 439 440 441 #> Row16 403 402 401 400 399 398 397 396 395 394 393 392 #> Row15 378 379 380 381 382 383 384 385 386 387 388 389 #> Row14 351 350 349 348 347 346 345 344 343 342 341 340 #> Row13 326 327 328 329 330 331 332 333 334 335 336 337 #> Row12 299 298 297 296 295 294 293 292 291 290 289 288 #> Row11 274 275 276 277 278 279 280 281 282 283 284 285 #> Row10 247 246 245 244 243 242 241 240 239 238 237 236 #> Row9 222 223 224 225 226 227 228 229 230 231 232 233 #> Row8 195 194 193 192 191 190 189 188 187 186 185 184 #> Row7 170 171 172 173 174 175 176 177 178 179 180 181 #> Row6 143 142 141 140 139 138 137 136 135 134 133 132 #> Row5 118 119 120 121 122 123 124 125 126 127 128 129 #> Row4 91 90 89 88 87 86 85 84 83 82 81 80 #> Row3 66 67 68 69 70 71 72 73 74 75 76 77 #> Row2 39 38 37 36 35 34 33 32 31 30 29 28 #> Row1 14 15 16 17 18 19 20 21 22 23 24 25 #> Col26 #> Row30 755 #> Row29 754 #> Row28 703 #> Row27 702 #> Row26 651 #> Row25 650 #> Row24 599 #> Row23 598 #> Row22 547 #> Row21 546 #> Row20 495 #> Row19 494 #> Row18 443 #> Row17 442 #> Row16 391 #> Row15 390 #> Row14 339 #> Row13 338 #> Row12 287 #> Row11 286 #> Row10 235 #> Row9 234 #> Row8 183 #> Row7 182 #> Row6 131 #> Row5 130 #> Row4 79 #> Row3 78 #> Row2 27 #> Row1 26 #> head(spatDB$fieldBook,12) #> ID EXPT LOCATION YEAR PLOT ROW COLUMN CHECKS ENTRY TREATMENT #> 1 1 Block1 1 2024 1 1 1 2 2 CH2 #> 2 2 Block1 1 2024 2 1 2 0 94 G94 #> 3 3 Block1 1 2024 3 1 3 0 102 G102 #> 4 4 Block1 1 2024 4 1 4 0 114 G114 #> 5 5 Block1 1 2024 5 1 5 0 26 G26 #> 6 6 Block1 1 2024 6 1 6 0 79 G79 #> 7 7 Block1 1 2024 7 1 7 0 91 G91 #> 8 8 Block1 1 2024 8 1 8 0 131 G131 #> 9 9 Block1 1 2024 9 1 9 0 25 G25 #> 10 10 Block1 1 2024 10 1 10 0 109 G109 #> 11 11 Block1 1 2024 11 1 11 0 8 G8 #> 12 12 Block1 1 2024 12 1 12 0 6 G6 # Example 3: Generates a spatial decision block diagonal arrangement design in one location # with 270 treatments allocated in 3 experiments or blocks for a field with dimensions # 20 rows x 15 cols in a serpentine arrangement. Which in turn is an augmented block (3 blocks). spatAB <- diagonal_arrangement( nrows = 20, ncols = 15, lines = 270, checks = 4, plotNumber = c(1,1001,2001), kindExpt = \"DBUDC\", planter = \"serpentine\", exptName = c(\"20WRA\", \"20WRB\", \"20WRC\"), blocks = c(90, 90, 90), splitBy = \"column\" ) spatAB$infoDesign #> $rows #> [1] 20 #> #> $columns #> [1] 15 #> #> $treatments #> [1] 90 90 90 #> #> $checks #> [1] 4 #> #> $entry_checks #> $entry_checks[[1]] #> [1] 1 2 3 4 #> #> #> $rep_checks #> $rep_checks[[1]] #> [1] 7 6 8 9 #> #> #> $locations #> [1] 1 #> #> $planter #> [1] \"serpentine\" #> #> $percent_checks #> [1] \"10%\" #> #> $fillers #> [1] 0 #> #> $seed #> [1] 72391 #> #> $id_design #> [1] 15 #> spatAB$layoutRandom #> [[1]] #> Col1 Col2 Col3 Col4 Col5 Col6 Col7 Col8 Col9 Col10 Col11 Col12 Col13 #> Row20 82 1 56 57 70 144 172 148 146 173 219 4 229 #> Row19 49 42 69 21 3 103 111 151 102 157 237 249 221 #> Row18 44 10 9 29 12 109 177 3 106 149 262 225 226 #> Row17 2 43 90 16 71 162 176 136 104 138 4 191 185 #> Row16 75 88 36 1 30 110 133 175 180 158 193 253 186 #> Row15 85 45 91 19 13 97 2 171 154 105 252 204 245 #> Row14 86 67 14 83 93 134 161 100 140 4 211 217 251 #> Row13 33 92 3 6 5 116 160 101 117 98 227 263 2 #> Row12 54 31 74 89 8 3 181 145 99 114 254 223 267 #> Row11 23 7 50 25 76 137 163 168 2 159 242 260 206 #> Row10 63 4 11 24 61 139 182 167 153 130 208 3 216 #> Row9 80 65 58 52 1 118 135 125 122 147 192 264 234 #> Row8 32 26 41 48 39 152 183 4 165 95 233 220 240 #> Row7 4 66 37 68 46 178 142 132 96 115 1 198 258 #> Row6 55 27 35 2 77 155 166 131 127 169 209 212 256 #> Row5 94 18 60 34 40 141 3 119 126 184 231 224 241 #> Row4 59 15 73 38 84 179 113 170 164 1 232 189 235 #> Row3 28 47 4 53 51 143 123 120 129 108 244 207 4 #> Row2 64 20 17 72 81 4 107 150 174 156 210 230 222 #> Row1 79 62 78 22 87 121 124 112 1 128 228 261 213 #> Col14 Col15 #> Row20 243 259 #> Row19 248 3 #> Row18 246 196 #> Row17 269 265 #> Row16 2 190 #> Row15 214 257 #> Row14 188 266 #> Row13 272 255 #> Row12 201 203 #> Row11 202 270 #> Row10 205 215 #> Row9 268 3 #> Row8 218 197 #> Row7 247 250 #> Row6 1 187 #> Row5 273 239 #> Row4 236 274 #> Row3 271 195 #> Row2 200 199 #> Row1 238 194 #> spatAB$plotsNumber #> [[1]] #> Col1 Col2 Col3 Col4 Col5 Col6 Col7 Col8 Col9 Col10 Col11 Col12 Col13 #> Row20 100 99 98 97 96 1100 1099 1098 1097 1096 2100 2099 2098 #> Row19 91 92 93 94 95 1091 1092 1093 1094 1095 2091 2092 2093 #> Row18 90 89 88 87 86 1090 1089 1088 1087 1086 2090 2089 2088 #> Row17 81 82 83 84 85 1081 1082 1083 1084 1085 2081 2082 2083 #> Row16 80 79 78 77 76 1080 1079 1078 1077 1076 2080 2079 2078 #> Row15 71 72 73 74 75 1071 1072 1073 1074 1075 2071 2072 2073 #> Row14 70 69 68 67 66 1070 1069 1068 1067 1066 2070 2069 2068 #> Row13 61 62 63 64 65 1061 1062 1063 1064 1065 2061 2062 2063 #> Row12 60 59 58 57 56 1060 1059 1058 1057 1056 2060 2059 2058 #> Row11 51 52 53 54 55 1051 1052 1053 1054 1055 2051 2052 2053 #> Row10 50 49 48 47 46 1050 1049 1048 1047 1046 2050 2049 2048 #> Row9 41 42 43 44 45 1041 1042 1043 1044 1045 2041 2042 2043 #> Row8 40 39 38 37 36 1040 1039 1038 1037 1036 2040 2039 2038 #> Row7 31 32 33 34 35 1031 1032 1033 1034 1035 2031 2032 2033 #> Row6 30 29 28 27 26 1030 1029 1028 1027 1026 2030 2029 2028 #> Row5 21 22 23 24 25 1021 1022 1023 1024 1025 2021 2022 2023 #> Row4 20 19 18 17 16 1020 1019 1018 1017 1016 2020 2019 2018 #> Row3 11 12 13 14 15 1011 1012 1013 1014 1015 2011 2012 2013 #> Row2 10 9 8 7 6 1010 1009 1008 1007 1006 2010 2009 2008 #> Row1 1 2 3 4 5 1001 1002 1003 1004 1005 2001 2002 2003 #> Col14 Col15 #> Row20 2097 2096 #> Row19 2094 2095 #> Row18 2087 2086 #> Row17 2084 2085 #> Row16 2077 2076 #> Row15 2074 2075 #> Row14 2067 2066 #> Row13 2064 2065 #> Row12 2057 2056 #> Row11 2054 2055 #> Row10 2047 2046 #> Row9 2044 2045 #> Row8 2037 2036 #> Row7 2034 2035 #> Row6 2027 2026 #> Row5 2024 2025 #> Row4 2017 2016 #> Row3 2014 2015 #> Row2 2007 2006 #> Row1 2004 2005 #> head(spatAB$fieldBook,12) #> ID EXPT LOCATION YEAR PLOT ROW COLUMN CHECKS ENTRY TREATMENT #> 1 1 20WRA 1 2024 1 1 1 0 79 Gen-79 #> 2 2 20WRA 1 2024 2 1 2 0 62 Gen-62 #> 3 3 20WRA 1 2024 3 1 3 0 78 Gen-78 #> 4 4 20WRA 1 2024 4 1 4 0 22 Gen-22 #> 5 5 20WRA 1 2024 5 1 5 0 87 Gen-87 #> 6 6 20WRB 1 2024 1001 1 6 0 121 Gen-121 #> 7 7 20WRB 1 2024 1002 1 7 0 124 Gen-124 #> 8 8 20WRB 1 2024 1003 1 8 0 112 Gen-112 #> 9 9 20WRB 1 2024 1004 1 9 1 1 Check-1 #> 10 10 20WRB 1 2024 1005 1 10 0 128 Gen-128 #> 11 11 20WRC 1 2024 2001 1 11 0 228 Gen-228 #> 12 12 20WRC 1 2024 2002 1 12 0 261 Gen-261"},{"path":"https://didiermurillof.github.io/FielDHub/reference/do_optim.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate the sparse or p-rep allocation to multiple locations. β€” do_optim","title":"Generate the sparse or p-rep allocation to multiple locations. β€” do_optim","text":"Generate sparse p-rep allocation multiple locations.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/do_optim.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate the sparse or p-rep allocation to multiple locations. β€” do_optim","text":"","code":"do_optim( design = \"sparse\", lines, l, copies_per_entry, add_checks = FALSE, checks = NULL, rep_checks = NULL, force_balance = TRUE, seed, data = NULL )"},{"path":"https://didiermurillof.github.io/FielDHub/reference/do_optim.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate the sparse or p-rep allocation to multiple locations. β€” do_optim","text":"design Type experimental design. can prep sparse lines Number genotypes, experimental lines treatments. l Number locations sites. default l = 1. copies_per_entry Number copies per plant. design sparse copies_per_entry less l add_checks Option add checks. Optional design = \"prep\" checks Number genotypes checks. rep_checks Replication check. force_balance Get balanced unbalanced locations. default force_balance = TRUE. seed (optional) Real number specifies starting seed obtain reproducible designs. data (optional) Data frame 2 columns: ENTRY | NAME . ENTRY must numeric.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/do_optim.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate the sparse or p-rep allocation to multiple locations. β€” do_optim","text":"list three elements. list_locs list location list entries. allocation matrix allocation treatments. size_locations data frame one column location one row size location.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/do_optim.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Generate the sparse or p-rep allocation to multiple locations. β€” do_optim","text":"Edmondson, R.N. Multi-level Block Designs Comparative Experiments. JABES 25, 500–522 (2020). https://doi.org/10.1007/s13253-020-00416-0","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/do_optim.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Generate the sparse or p-rep allocation to multiple locations. β€” do_optim","text":"Didier Murillo [aut], Salvador Gezan [aut], Ana Heilman [ctb]","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/do_optim.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate the sparse or p-rep allocation to multiple locations. β€” do_optim","text":"","code":"sparse_example <- do_optim( design = \"sparse\", lines = 120, l = 4, copies_per_entry = 3, add_checks = TRUE, checks = 4, seed = 15 )"},{"path":"https://didiermurillof.github.io/FielDHub/reference/full_factorial.html","id":null,"dir":"Reference","previous_headings":"","what":"Generates a Full Factorial Design β€” full_factorial","title":"Generates a Full Factorial Design β€” full_factorial","text":"randomly generates full factorial design across locations.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/full_factorial.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generates a Full Factorial Design β€” full_factorial","text":"","code":"full_factorial( setfactors = NULL, reps = NULL, l = 1, type = 2, plotNumber = 101, continuous = FALSE, planter = \"serpentine\", seed = NULL, locationNames = NULL, factorLabels = TRUE, data = NULL )"},{"path":"https://didiermurillof.github.io/FielDHub/reference/full_factorial.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generates a Full Factorial Design β€” full_factorial","text":"setfactors Numeric vector levels factor. reps Number replicates (full blocks). l Number locations. default l = 1. type Option CRD RCBD designs. Values type = 1 (CRD) type = 2 (RCBD). default type = 2. plotNumber Numeric vector starting plot number location. default plotNumber = 101. continuous Logical plot number continuous . default continuous = FALSE. planter Option serpentine cartesian plot arrangement. default planter = 'serpentine'. seed (optional) Real number specifies starting seed obtain reproducible designs. locationNames (optional) Names location. factorLabels (optional) TRUE retain levels labels original data set otherwise, numeric labels assigned. Default factorLabels =TRUE. data (optional) Data frame labels factors.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/full_factorial.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generates a Full Factorial Design β€” full_factorial","text":"list two elements. infoDesign list information design parameters. fieldBook data frame full factorial field book.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/full_factorial.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Generates a Full Factorial Design β€” full_factorial","text":"Federer, W. T. (1955). Experimental Design. Theory Application. New York, USA. Macmillan Company.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/full_factorial.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Generates a Full Factorial Design β€” full_factorial","text":"Didier Murillo [aut], Salvador Gezan [aut], Ana Heilman [ctb], Thomas Walk [ctb], Johan Aparicio [ctb], Richard Horsley [ctb]","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/full_factorial.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generates a Full Factorial Design β€” full_factorial","text":"","code":"# Example 1: Generates a full factorial with 3 factors each with 2 levels. # This in an RCBD arrangement with 3 reps. fullFact1 <- full_factorial(setfactors = c(2,2,2), reps = 3, l = 1, type = 2, plotNumber = 101, continuous = TRUE, planter = \"serpentine\", seed = 325, locationNames = \"FARGO\") fullFact1$infoDesign #> $factors #> [1] \"A\" \"B\" \"C\" #> #> $levels #> [1] 0 1 0 1 0 1 #> #> $runs #> [1] 8 #> #> $all_treatments #> A B C #> 1 0 0 0 #> 2 1 0 0 #> 3 0 1 0 #> 4 1 1 0 #> 5 0 0 1 #> 6 1 0 1 #> 7 0 1 1 #> 8 1 1 1 #> #> $reps #> [1] 3 #> #> $locations #> [1] 1 #> #> $location_names #> [1] \"FARGO\" #> #> $kind #> [1] \"RCBD\" #> #> $levels_each_factor #> [1] 2 2 2 #> #> $id_design #> [1] 4 #> head(fullFact1$fieldBook,10) #> ID LOCATION PLOT REP FACTOR_A FACTOR_B FACTOR_C TRT_COMB #> 1 1 FARGO 101 1 0 1 1 0*1*1 #> 2 2 FARGO 102 1 1 1 1 1*1*1 #> 3 3 FARGO 103 1 1 0 0 1*0*0 #> 4 4 FARGO 104 1 0 1 0 0*1*0 #> 5 5 FARGO 105 1 1 1 0 1*1*0 #> 6 6 FARGO 106 1 1 0 1 1*0*1 #> 7 7 FARGO 107 1 0 0 0 0*0*0 #> 8 8 FARGO 108 1 0 0 1 0*0*1 #> 16 9 FARGO 109 2 1 1 0 1*1*0 #> 15 10 FARGO 110 2 0 0 0 0*0*0 # Example 2: Generates a full factorial with 3 factors and each with levels: 2,3, # and 2, respectively. In this case, we show how to use the option data FACTORS <- rep(c(\"A\", \"B\", \"C\"), c(2,3,2)) LEVELS <- c(\"a0\", \"a1\", \"b0\", \"b1\", \"b2\", \"c0\", \"c1\") data_factorial <- data.frame(list(FACTOR = FACTORS, LEVEL = LEVELS)) print(data_factorial) #> FACTOR LEVEL #> 1 A a0 #> 2 A a1 #> 3 B b0 #> 4 B b1 #> 5 B b2 #> 6 C c0 #> 7 C c1 # This in an RCBD arrangement with 5 reps in 3 locations. fullFact2 <- full_factorial(setfactors = NULL, reps = 5, l = 3, type = 2, plotNumber = c(101,1001,2001), continuous = FALSE, planter = \"serpentine\", seed = 326, locationNames = c(\"Loc1\",\"Loc2\",\"Loc3\"), data = data_factorial) fullFact2$infoDesign #> $factors #> [1] \"A\" \"B\" \"C\" #> #> $levels #> $levels[[1]] #> [1] \"a0\" \"a1\" #> #> $levels[[2]] #> [1] \"b0\" \"b1\" \"b2\" #> #> $levels[[3]] #> [1] \"c0\" \"c1\" #> #> #> $runs #> [1] 12 #> #> $all_treatments #> A B C #> 1 a0 b0 c0 #> 2 a1 b0 c0 #> 3 a0 b1 c0 #> 4 a1 b1 c0 #> 5 a0 b2 c0 #> 6 a1 b2 c0 #> 7 a0 b0 c1 #> 8 a1 b0 c1 #> 9 a0 b1 c1 #> 10 a1 b1 c1 #> 11 a0 b2 c1 #> 12 a1 b2 c1 #> #> $reps #> [1] 5 #> #> $locations #> [1] 3 #> #> $location_names #> [1] \"Loc1\" \"Loc2\" \"Loc3\" #> #> $kind #> [1] \"RCBD\" #> #> $levels_each_factor #> [1] 2 3 2 #> #> $id_design #> [1] 4 #> head(fullFact2$fieldBook,10) #> ID LOCATION PLOT REP FACTOR_A FACTOR_B FACTOR_C TRT_COMB #> 1 1 Loc1 101 1 a0 b1 c0 a0*b1*c0 #> 2 2 Loc1 102 1 a1 b0 c1 a1*b0*c1 #> 3 3 Loc1 103 1 a1 b2 c1 a1*b2*c1 #> 4 4 Loc1 104 1 a0 b1 c1 a0*b1*c1 #> 5 5 Loc1 105 1 a1 b0 c0 a1*b0*c0 #> 6 6 Loc1 106 1 a0 b0 c1 a0*b0*c1 #> 7 7 Loc1 107 1 a1 b1 c0 a1*b1*c0 #> 8 8 Loc1 108 1 a0 b2 c1 a0*b2*c1 #> 9 9 Loc1 109 1 a1 b1 c1 a1*b1*c1 #> 10 10 Loc1 110 1 a0 b0 c0 a0*b0*c0"},{"path":"https://didiermurillof.github.io/FielDHub/reference/incomplete_blocks.html","id":null,"dir":"Reference","previous_headings":"","what":"Generates a Resolvable Incomplete Block Design β€” incomplete_blocks","title":"Generates a Resolvable Incomplete Block Design β€” incomplete_blocks","text":"Randomly generates resolvable incomplete block design (IBD) characteristics (t, k, r). randomization can done across locations.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/incomplete_blocks.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generates a Resolvable Incomplete Block Design β€” incomplete_blocks","text":"","code":"incomplete_blocks( t = NULL, k = NULL, r = NULL, l = 1, plotNumber = 101, locationNames = NULL, seed = NULL, data = NULL )"},{"path":"https://didiermurillof.github.io/FielDHub/reference/incomplete_blocks.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generates a Resolvable Incomplete Block Design β€” incomplete_blocks","text":"t Number treatments. k Size incomplete blocks (number units per incomplete block). r Number full blocks (resolvable replicates) (also number replicates per treatment). l Number locations. default l = 1. plotNumber Numeric vector starting plot number location. default plotNumber = 101. locationNames (optional) Names location. seed (optional) Real number specifies starting seed obtain reproducible designs. data (optional) Data frame label list treatments.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/incomplete_blocks.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generates a Resolvable Incomplete Block Design β€” incomplete_blocks","text":"list two elements. infoDesign list information design parameters. fieldBook data frame incomplete block design field book.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/incomplete_blocks.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Generates a Resolvable Incomplete Block Design β€” incomplete_blocks","text":"Edmondson., R. N. (2021). blocksdesign: Nested crossed block designs factorial unstructured treatment sets. https://CRAN.R-project.org/package=blocksdesign","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/incomplete_blocks.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Generates a Resolvable Incomplete Block Design β€” incomplete_blocks","text":"Didier Murillo [aut], Salvador Gezan [aut], Ana Heilman [ctb], Thomas Walk [ctb], Johan Aparicio [ctb], Richard Horsley [ctb]","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/incomplete_blocks.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generates a Resolvable Incomplete Block Design β€” incomplete_blocks","text":"","code":"# Example 1: Generates a resolvable IBD of characteristics (t,k,r) = (12,4,2). # 1-resolvable IBDs ibd1 <- incomplete_blocks(t = 12, k = 4, r = 2, seed = 1984) ibd1$infoDesign #> $Reps #> [1] 2 #> #> $iBlocks #> [1] 3 #> #> $NumberTreatments #> [1] 12 #> #> $NumberLocations #> [1] 1 #> #> $Locations #> [1] 1 #> #> $seed #> [1] 1984 #> #> $lambda #> [1] 0.5454545 #> #> $id_design #> [1] 8 #> head(ibd1$fieldBook) #> ID LOCATION PLOT REP IBLOCK UNIT ENTRY TREATMENT #> 1 1 1 101 1 1 1 9 G-9 #> 2 2 1 102 1 1 2 5 G-5 #> 3 3 1 103 1 1 3 6 G-6 #> 4 4 1 104 1 1 4 12 G-12 #> 5 5 1 105 1 2 1 2 G-2 #> 6 6 1 106 1 2 2 11 G-11 # Example 2: Generates a balanced resolvable IBD of characteristics (t,k,r) = (15,3,7). # In this case, we show how to use the option data. treatments <- paste(\"TX-\", 1:15, sep = \"\") ENTRY <- 1:15 treatment_list <- data.frame(list(ENTRY = ENTRY, TREATMENT = treatments)) head(treatment_list) #> ENTRY TREATMENT #> 1 1 TX-1 #> 2 2 TX-2 #> 3 3 TX-3 #> 4 4 TX-4 #> 5 5 TX-5 #> 6 6 TX-6 ibd2 <- incomplete_blocks(t = 15, k = 3, r = 7, seed = 1985, data = treatment_list) ibd2$infoDesign #> $Reps #> [1] 7 #> #> $iBlocks #> [1] 5 #> #> $NumberTreatments #> [1] 15 #> #> $NumberLocations #> [1] 1 #> #> $Locations #> [1] 1 #> #> $seed #> [1] 1985 #> #> $lambda #> [1] 1 #> #> $id_design #> [1] 8 #> head(ibd2$fieldBook) #> ID LOCATION PLOT REP IBLOCK UNIT ENTRY TREATMENT #> 1 1 1 101 1 1 1 1 TX-1 #> 2 2 1 102 1 1 2 11 TX-11 #> 3 3 1 103 1 1 3 13 TX-13 #> 4 4 1 104 1 2 1 3 TX-3 #> 5 5 1 105 1 2 2 14 TX-14 #> 6 6 1 106 1 2 3 4 TX-4"},{"path":"https://didiermurillof.github.io/FielDHub/reference/latin_square.html","id":null,"dir":"Reference","previous_headings":"","what":"Generates a Latin Square Design β€” latin_square","title":"Generates a Latin Square Design β€” latin_square","text":"Randomly generates latin square design 10 treatments.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/latin_square.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generates a Latin Square Design β€” latin_square","text":"","code":"latin_square( t = NULL, reps = 1, plotNumber = 101, planter = \"serpentine\", seed = NULL, locationNames = NULL, data = NULL )"},{"path":"https://didiermurillof.github.io/FielDHub/reference/latin_square.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generates a Latin Square Design β€” latin_square","text":"t Number treatments. reps Number full resolvable squares. default reps = 1. plotNumber Starting plot number. default plotNumber = 101. planter Option serpentine cartesian arrangement. default planter = 'serpentine'. seed (optional) Real number specifies starting seed obtain reproducible designs. locationNames (optional) Name location. data (optional) Data frame label list treatments.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/latin_square.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generates a Latin Square Design β€” latin_square","text":"list information design parameters. Data frame latin square field book. list two elements. infoDesign list information design parameters. fieldBook data frame latin square field book.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/latin_square.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Generates a Latin Square Design β€” latin_square","text":"Federer, W. T. (1955). Experimental Design. Theory Application. New York, USA. Macmillan Company.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/latin_square.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Generates a Latin Square Design β€” latin_square","text":"Didier Murillo [aut], Salvador Gezan [aut], Ana Heilman [ctb], Thomas Walk [ctb], Johan Aparicio [ctb], Thiago de Paula Oliveira[ctb] Richard Horsley [ctb]","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/latin_square.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generates a Latin Square Design β€” latin_square","text":"","code":"# Example 1: Generates a latin square design with 4 treatments and 2 reps. latinSq1 <- latin_square(t = 4, reps = 2, plotNumber = 101, planter = \"cartesian\", seed = 1980) print(latinSq1) #> Latin Square Design: #> #> Information on the design parameters: #> List of 4 #> $ treatments : int 4 #> $ squares : num 2 #> $ locationName: NULL #> $ seed : num 1980 #> #> 10 First observations of the data frame with the latin_square field book: #> ID LOCATION PLOT SQUARE ROW COLUMN TREATMENT #> 1 1 1 101 1 Row 1 Column 1 T1 #> 2 2 1 102 1 Row 1 Column 2 T4 #> 3 3 1 103 1 Row 1 Column 3 T2 #> 4 4 1 104 1 Row 1 Column 4 T3 #> 5 5 1 105 1 Row 2 Column 1 T3 #> 6 6 1 106 1 Row 2 Column 2 T1 #> 7 7 1 107 1 Row 2 Column 3 T4 #> 8 8 1 108 1 Row 2 Column 4 T2 #> 9 9 1 109 1 Row 3 Column 1 T4 #> 10 10 1 110 1 Row 3 Column 2 T2 summary(latinSq1) #> Latin Square Design: #> #> 1. Information on the design parameters: #> List of 5 #> $ treatments : int 4 #> $ squares : num 2 #> $ locationName: NULL #> $ seed : num 1980 #> $ id_design : num 3 #> #> 2. Squares: #> $rep1 #> Column 1 Column 2 Column 3 Column 4 #> Row 1 \"T1\" \"T4\" \"T2\" \"T3\" #> Row 2 \"T3\" \"T1\" \"T4\" \"T2\" #> Row 3 \"T4\" \"T2\" \"T3\" \"T1\" #> Row 4 \"T2\" \"T3\" \"T1\" \"T4\" #> #> $rep2 #> Column 1 Column 2 Column 3 Column 4 #> Row 1 \"T1\" \"T3\" \"T4\" \"T2\" #> Row 2 \"T2\" \"T4\" \"T3\" \"T1\" #> Row 3 \"T4\" \"T1\" \"T2\" \"T3\" #> Row 4 \"T3\" \"T2\" \"T1\" \"T4\" #> #> #> 3. Plot squares: #> $rep1 #> [,1] [,2] [,3] [,4] #> [1,] 101 102 103 104 #> [2,] 105 106 107 108 #> [3,] 109 110 111 112 #> [4,] 113 114 115 116 #> #> $rep2 #> [,1] [,2] [,3] [,4] #> [1,] 201 202 203 204 #> [2,] 205 206 207 208 #> [3,] 209 210 211 212 #> [4,] 213 214 215 216 #> #> #> 4. Structure of the data frame with the latin_square field book: #> #> 'data.frame':\t32 obs. of 7 variables: #> $ ID : int 1 2 3 4 5 6 7 8 9 10 ... #> $ LOCATION : int 1 1 1 1 1 1 1 1 1 1 ... #> $ PLOT : int 101 102 103 104 105 106 107 108 109 110 ... #> $ SQUARE : int 1 1 1 1 1 1 1 1 1 1 ... #> $ ROW : Factor w/ 4 levels \"Row 1\",\"Row 2\",..: 1 1 1 1 2 2 2 2 3 3 ... #> $ COLUMN : Factor w/ 4 levels \"Column 1\",\"Column 2\",..: 1 2 3 4 1 2 3 4 1 2 ... #> $ TREATMENT: chr \"T1\" \"T4\" \"T2\" \"T3\" ... head(latinSq1$fieldBook) #> ID LOCATION PLOT SQUARE ROW COLUMN TREATMENT #> 1 1 1 101 1 Row 1 Column 1 T1 #> 2 2 1 102 1 Row 1 Column 2 T4 #> 3 3 1 103 1 Row 1 Column 3 T2 #> 4 4 1 104 1 Row 1 Column 4 T3 #> 5 5 1 105 1 Row 2 Column 1 T3 #> 6 6 1 106 1 Row 2 Column 2 T1 # Example 2: Generates a latin square design with 5 treatments and 3 reps. latin_data <- data.frame(list(ROW = paste(\"Period\", 1:5, sep = \"\"), COLUMN = paste(\"Cow\", 1:5, sep = \"\"), TREATMENT = paste(\"Diet\", 1:5, sep = \"\"))) print(latin_data) #> ROW COLUMN TREATMENT #> 1 Period1 Cow1 Diet1 #> 2 Period2 Cow2 Diet2 #> 3 Period3 Cow3 Diet3 #> 4 Period4 Cow4 Diet4 #> 5 Period5 Cow5 Diet5 latinSq2 <- latin_square(t = NULL, reps = 3, plotNumber = 101, planter = \"cartesian\", seed = 1981, data = latin_data) latinSq2$squares #> $rep1 #> Cow1 Cow2 Cow3 Cow4 Cow5 #> Period1 \"Diet4\" \"Diet3\" \"Diet2\" \"Diet1\" \"Diet5\" #> Period2 \"Diet1\" \"Diet2\" \"Diet4\" \"Diet5\" \"Diet3\" #> Period3 \"Diet3\" \"Diet5\" \"Diet1\" \"Diet2\" \"Diet4\" #> Period4 \"Diet2\" \"Diet4\" \"Diet5\" \"Diet3\" \"Diet1\" #> Period5 \"Diet5\" \"Diet1\" \"Diet3\" \"Diet4\" \"Diet2\" #> #> $rep2 #> Cow1 Cow2 Cow3 Cow4 Cow5 #> Period1 \"Diet2\" \"Diet4\" \"Diet5\" \"Diet3\" \"Diet1\" #> Period2 \"Diet1\" \"Diet2\" \"Diet3\" \"Diet4\" \"Diet5\" #> Period3 \"Diet3\" \"Diet5\" \"Diet1\" \"Diet2\" \"Diet4\" #> Period4 \"Diet4\" \"Diet1\" \"Diet2\" \"Diet5\" \"Diet3\" #> Period5 \"Diet5\" \"Diet3\" \"Diet4\" \"Diet1\" \"Diet2\" #> #> $rep3 #> Cow1 Cow2 Cow3 Cow4 Cow5 #> Period1 \"Diet4\" \"Diet2\" \"Diet1\" \"Diet3\" \"Diet5\" #> Period2 \"Diet1\" \"Diet3\" \"Diet2\" \"Diet5\" \"Diet4\" #> Period3 \"Diet3\" \"Diet5\" \"Diet4\" \"Diet2\" \"Diet1\" #> Period4 \"Diet5\" \"Diet1\" \"Diet3\" \"Diet4\" \"Diet2\" #> Period5 \"Diet2\" \"Diet4\" \"Diet5\" \"Diet1\" \"Diet3\" #> latinSq2$plotSquares #> $rep1 #> [,1] [,2] [,3] [,4] [,5] #> [1,] 101 102 103 104 105 #> [2,] 106 107 108 109 110 #> [3,] 111 112 113 114 115 #> [4,] 116 117 118 119 120 #> [5,] 121 122 123 124 125 #> #> $rep2 #> [,1] [,2] [,3] [,4] [,5] #> [1,] 201 202 203 204 205 #> [2,] 206 207 208 209 210 #> [3,] 211 212 213 214 215 #> [4,] 216 217 218 219 220 #> [5,] 221 222 223 224 225 #> #> $rep3 #> [,1] [,2] [,3] [,4] [,5] #> [1,] 301 302 303 304 305 #> [2,] 306 307 308 309 310 #> [3,] 311 312 313 314 315 #> [4,] 316 317 318 319 320 #> [5,] 321 322 323 324 325 #> head(latinSq2$fieldBook) #> ID LOCATION PLOT SQUARE ROW COLUMN TREATMENT #> 1 1 1 101 1 Period1 Cow1 Diet4 #> 2 2 1 102 1 Period1 Cow2 Diet3 #> 3 3 1 103 1 Period1 Cow3 Diet2 #> 4 4 1 104 1 Period1 Cow4 Diet1 #> 5 5 1 105 1 Period1 Cow5 Diet5 #> 6 6 1 106 1 Period2 Cow1 Diet1"},{"path":"https://didiermurillof.github.io/FielDHub/reference/multi_location_prep.html","id":null,"dir":"Reference","previous_headings":"","what":"Optimized multi-location partially replicated design β€” multi_location_prep","title":"Optimized multi-location partially replicated design β€” multi_location_prep","text":"Optimized multi-location partially replicated design","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/multi_location_prep.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Optimized multi-location partially replicated design β€” multi_location_prep","text":"","code":"multi_location_prep( lines, nrows, ncols, l, planter = \"serpentine\", plotNumber, desired_avg, copies_per_entry, checks = NULL, rep_checks = NULL, exptName, locationNames, optim_list, seed, data = NULL )"},{"path":"https://didiermurillof.github.io/FielDHub/reference/multi_location_prep.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Optimized multi-location partially replicated design β€” multi_location_prep","text":"lines Number genotypes, experimental lines treatments. nrows Numeric vector number rows field location. ncols Numeric vector number columns field location. l Number locations. default l = 1. planter Option serpentine cartesian movement. default planter = 'serpentine'. plotNumber Numeric vector starting plot number location. default plotNumber = 101. desired_avg (optional) Desired average treatments across locations. copies_per_entry Number total copies per treatment. checks Number checks. rep_checks Number replications per check. exptName (optional) Name experiment. locationNames (optional) Name location. optim_list (optional) list object class \"MultiPrep\"generated do_optim() function. seed (optional) Real number specifies starting seed obtain reproducible designs. data (optional) Data frame 2 columns: ENTRY | NAME . ENTRY must numeric.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/multi_location_prep.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Optimized multi-location partially replicated design β€” multi_location_prep","text":"list class FielDHub several elements. infoDesign list information design parameters. layoutRandom matrix randomization layout. plotNumber matrix layout plot number. binaryField matrix binary field. dataEntry data frame data input. genEntries list entries replicated non-replicated parts. fieldBook data frame field book design. includes index (Row, Column). min_pairwise_distance data frame minimum pairwise distance pair locations. reps_info data frame information number replicated non-replicated treatments location. pairsDistance data frame pairwise distances pair treatments. treatments_with_reps list entries replicated part design. treatments_with_no_reps list entries non-replicated part design. list_locs list location list entries. allocation matrix allocation treatments. size_locations data frame one column location one row size location.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/multi_location_prep.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Optimized multi-location partially replicated design β€” multi_location_prep","text":"Edmondson, R.N. Multi-level Block Designs Comparative Experiments. JABES 25, 500–522 (2020). https://doi.org/10.1007/s13253-020-00416-0","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/multi_location_prep.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Optimized multi-location partially replicated design β€” multi_location_prep","text":"Didier Murillo [aut], Salvador Gezan [aut], Jean-Marc Montpetit [ctb], Ana Heilman [ctb]","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/multi_location_prep.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Optimized multi-location partially replicated design β€” multi_location_prep","text":"","code":"# Example 1: Generates a spatially optimized multi-location p-rep design with 142 # genotypes. The number of copies per plant available for this experiment is 9. # This experiment is carried out in 5 locations, and there are seven seeds available # for each plant to make replications. # In this case, we add three controls (checks) with six reps each. # With this setup, the experiment will have 142 treatments + 3 checks = 145 # entries and the number of plots per location after the allocation process # will be 196. # The average genotype allocation will be 1.5 copies per location. if (FALSE) { # \\dontrun{ optim_multi_prep <- multi_location_prep( lines = 150, l = 5, copies_per_entry = 7, checks = 3, rep_checks = c(6,6,6), locationNames = c(\"LOC1\", \"LOC2\", \"LOC3\", \"LOC4\", \"LOC5\"), seed = 1234 ) designs <- optim_multi_prep$designs field_book_loc_1 <- designs$LOC1$fieldBook head(field_book_loc_1, 10) } # }"},{"path":"https://didiermurillof.github.io/FielDHub/reference/optimized_arrangement.html","id":null,"dir":"Reference","previous_headings":"","what":"Generates an Spatial Un-replicated Optimized Arrangement Design β€” optimized_arrangement","title":"Generates an Spatial Un-replicated Optimized Arrangement Design β€” optimized_arrangement","text":"Randomly generates spatial un-replicated optimized arrangement design, distance checks maximized way row column control plots. Note design generation needs dimension field (number rows columns).","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/optimized_arrangement.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generates an Spatial Un-replicated Optimized Arrangement Design β€” optimized_arrangement","text":"","code":"optimized_arrangement( nrows = NULL, ncols = NULL, lines = NULL, amountChecks = NULL, checks = NULL, planter = \"serpentine\", l = 1, plotNumber = 101, seed = NULL, exptName = NULL, locationNames = NULL, optim = TRUE, data = NULL )"},{"path":"https://didiermurillof.github.io/FielDHub/reference/optimized_arrangement.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generates an Spatial Un-replicated Optimized Arrangement Design β€” optimized_arrangement","text":"nrows Number rows field. ncols Number columns field. lines Number genotypes, experimental lines treatments. amountChecks Integer amount total checks numeric vector replicates check label. checks Number genotypes checks. planter Option serpentine cartesian arrangement. default planter = 'serpentine'. l Number locations. default l = 1. plotNumber Numeric vector starting plot number location. default plotNumber = 101. seed (optional) Real number specifies starting seed obtain reproducible designs. exptName (optional) Name experiment. locationNames (optional) Name location. optim default optim = TRUE. data (optional) Data frame 3 columns: ENTRY | NAME | REPS.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/optimized_arrangement.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generates an Spatial Un-replicated Optimized Arrangement Design β€” optimized_arrangement","text":"list five elements. infoDesign list information design parameters. layoutRandom matrix randomization layout. plotNumber matrix layout plot number. dataEntry data frame data input. genEntries list entries replicated replicated part. fieldBook data frame field book design. includes index (Row, Column).","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/optimized_arrangement.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Generates an Spatial Un-replicated Optimized Arrangement Design β€” optimized_arrangement","text":"Clarke, G. P. Y., & Stefanova, K. T. (2011). Optimal design early-generation plant breeding trials unreplicated partially replicated test lines. Australian & New Zealand Journal Statistics, 53(4), 461–480.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/optimized_arrangement.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Generates an Spatial Un-replicated Optimized Arrangement Design β€” optimized_arrangement","text":"Didier Murillo [aut], Salvador Gezan [aut], Ana Heilman [ctb], Thomas Walk [ctb], Johan Aparicio [ctb], Richard Horsley [ctb]","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/optimized_arrangement.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generates an Spatial Un-replicated Optimized Arrangement Design β€” optimized_arrangement","text":"","code":"# Example 1: Generates a spatial unreplicated optimized arrangement design in one location # with 120 genotypes + 20 check plots (4 checks) for a field with dimension 14 rows x 10 cols. if (FALSE) { # \\dontrun{ optim_unrep1 <- optimized_arrangement( nrows = 14, ncols = 10, lines = 120, amountChecks = 20, checks = 1:4, planter = \"cartesian\", plotNumber = 101, exptName = \"20RW1\", locationNames = \"CASSELTON\", seed = 14124 ) optim_unrep1$infoDesign optim_unrep1$layoutRandom optim_unrep1$plotNumber head(optim_unrep1$fieldBook, 12) } # } # Example 2: Generates a spatial unreplicated optimized arrangement design in one location # with 200 genotypes + 20 check plots (4 checks) for a field with dimension 10 rows x 22 cols. # As example, we set up the data option with the entries list. if (FALSE) { # \\dontrun{ checks <- 4 list_checks <- paste(\"CH\", 1:checks, sep = \"\") treatments <- paste(\"G\", 5:204, sep = \"\") REPS <- c(5, 5, 5, 5, rep(1, 200)) treatment_list <- data.frame(list(ENTRY = 1:204, NAME = c(list_checks, treatments), REPS = REPS)) head(treatment_list, 12) tail(treatment_list, 12) optim_unrep2 <- optimized_arrangement( nrows = 10, ncols = 22, planter = \"serpentine\", plotNumber = 101, seed = 120, exptName = \"20YWA2\", locationNames = \"MINOT\", data = treatment_list ) optim_unrep2$infoDesign optim_unrep2$layoutRandom optim_unrep2$plotNumber head(optim_unrep2$fieldBook,12) } # }"},{"path":"https://didiermurillof.github.io/FielDHub/reference/partially_replicated.html","id":null,"dir":"Reference","previous_headings":"","what":"Generates a Spatial Partially Replicated Arrangement Design β€” partially_replicated","title":"Generates a Spatial Partially Replicated Arrangement Design β€” partially_replicated","text":"Randomly generates spatial partially replicated (p-rep) design single multiple locations.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/partially_replicated.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generates a Spatial Partially Replicated Arrangement Design β€” partially_replicated","text":"","code":"partially_replicated( nrows = NULL, ncols = NULL, repGens = NULL, repUnits = NULL, planter = \"serpentine\", l = 1, plotNumber = 101, seed = NULL, exptName = NULL, locationNames = NULL, multiLocationData = FALSE, data = NULL )"},{"path":"https://didiermurillof.github.io/FielDHub/reference/partially_replicated.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generates a Spatial Partially Replicated Arrangement Design β€” partially_replicated","text":"nrows Numeric vector number rows field location. ncols Numeric vector number columns field location. repGens Numeric vector amount genotypes replicate. repUnits Numeric vector number reps genotype. planter Option serpentine cartesian movement. default planter = 'serpentine'. l Number locations. default l = 1. plotNumber Numeric vector starting plot number location. default plotNumber = 101. seed (optional) Real number specifies starting seed obtain reproducible designs. exptName (optional) Name experiment. locationNames (optional) Name location. multiLocationData (optional) Option pass entry list multiple locations. default multiLocationData = FALSE. data (optional) Data frame 3 columns: ENTRY | NAME | REPS. multiLocationData = TRUE data must 4 columns: LOCATION | ENTRY | NAME | REPS","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/partially_replicated.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generates a Spatial Partially Replicated Arrangement Design β€” partially_replicated","text":"list several elements. infoDesign list information design parameters. layoutRandom matrix randomization layout. plotNumber matrix layout plot number. binaryField matrix binary field. dataEntry data frame data input. genEntries list entries replicated non-replicated parts. fieldBook data frame field book design. includes index (Row, Column). min_pairwise_distance data frame minimum pairwise distance pair locations. reps_info data frame information number replicated non-replicated treatments location. pairsDistance data frame pairwise distances pair treatments. treatments_with_reps list entries replicated part design. treatments_with_no_reps list entries non-replicated part design.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/partially_replicated.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generates a Spatial Partially Replicated Arrangement Design β€” partially_replicated","text":"function generates optimizes partially replicated (p-rep) experimental design given set treatments replication levels. design represented matrix optimized using pairwise distance metric. function outputs various information optimized design including field layout, replicated unreplicated treatments, pairwise distances treatments. Note design generation needs dimension field (number rows columns).","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/partially_replicated.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Generates a Spatial Partially Replicated Arrangement Design β€” partially_replicated","text":"Cullis, S., B. R., & Coombes, N. E. (2006). design early generation variety trials correlated data. Journal Agricultural, Biological, Environmental Statistics, 11, 381–393. https://doi.org/10.1198/108571106X154443","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/partially_replicated.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Generates a Spatial Partially Replicated Arrangement Design β€” partially_replicated","text":"Didier Murillo [aut], Salvador Gezan [aut], Ana Heilman [ctb], Thomas Walk [ctb], Johan Aparicio [ctb], Jean-Marc Montpetit [ctb], Richard Horsley [ctb]","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/partially_replicated.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generates a Spatial Partially Replicated Arrangement Design β€” partially_replicated","text":"","code":"# Example 1: Generates a spatial optimized partially replicated arrangement design in one # location with 335 genotypes for a field with dimensions 15 rows x 28 cols. # Note that there are 250 genotypes unreplicated (only one time), 85 genotypes replicated # two times, and three checks 8 times each. if (FALSE) { # \\dontrun{ prep_deseign1 <- partially_replicated( nrows = 12, ncols = 37, repGens = c(250, 85, 3), repUnits = c(1, 2, 8), planter = \"cartesian\", plotNumber = 101, seed = 77 ) prep_deseign1$infoDesign prep_deseign1$layoutRandom prep_deseign1$plotNumber head(prep_deseign1$fieldBook, 12) } # } # Example 2: Generates a spatial optimized partially replicated arrangement design with 492 # genotypes in a field with dimensions 30 rows x 20 cols. Note that there 384 genotypes # unreplicated (only one time), 108 genotypes replicated two times. # In this case we don't have check plots. # As example, we set up the data option with the entries list. if (FALSE) { # \\dontrun{ NAME <- paste(\"G\", 1:492, sep = \"\") repGens = c(108, 384);repUnits = c(2,1) REPS <- rep(repUnits, repGens) treatment_list <- data.frame(list(ENTRY = 1:492, NAME = NAME, REPS = REPS)) head(treatment_list, 12) tail(treatment_list, 12) prep_deseign2 <- partially_replicated( nrows = 30, ncols = 20, planter = \"serpentine\", plotNumber = 101, seed = 41, data = treatment_list ) prep_deseign2$infoDesign prep_deseign2$layoutRandom prep_deseign2$plotNumber head(prep_deseign2$fieldBook, 10) } # }"},{"path":"https://didiermurillof.github.io/FielDHub/reference/plot.FielDHub.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot a FielDHub object β€” plot.FielDHub","title":"Plot a FielDHub object β€” plot.FielDHub","text":"Draw field layout plot FielDHub object.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/plot.FielDHub.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot a FielDHub object β€” plot.FielDHub","text":"","code":"# S3 method for class 'FielDHub' plot(x, ...)"},{"path":"https://didiermurillof.github.io/FielDHub/reference/plot.FielDHub.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot a FielDHub object β€” plot.FielDHub","text":"x object inheriting class FielDHub ... arguments passed utility function plot_layout(). layout integer. Options available depend type design characteristics l integer specify location plot. planter can serpentine cartesian. stacked can vertical horizontal stacked layout.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/plot.FielDHub.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot a FielDHub object β€” plot.FielDHub","text":"plot object inheriting class fieldLayout field_book data frame fieldbook includes coordinates ROW COLUMN.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/plot.FielDHub.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot a FielDHub object β€” plot.FielDHub","text":"Didier Murillo [aut]","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/plot.FielDHub.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot a FielDHub object β€” plot.FielDHub","text":"","code":"if (FALSE) { # \\dontrun{ # Example 1: Plot a RCBD design with 24 treatments and 3 reps. s <- RCBD(t = 24, reps = 3, plotNumber = 101, seed = 12) plot(s) } # }"},{"path":"https://didiermurillof.github.io/FielDHub/reference/print.FielDHub.html","id":null,"dir":"Reference","previous_headings":"","what":"Print a FielDHub object β€” print.FielDHub","title":"Print a FielDHub object β€” print.FielDHub","text":"Prints information FielDHub function.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/print.FielDHub.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print a FielDHub object β€” print.FielDHub","text":"","code":"# S3 method for class 'FielDHub' print(x, n, ...)"},{"path":"https://didiermurillof.github.io/FielDHub/reference/print.FielDHub.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print a FielDHub object β€” print.FielDHub","text":"x object inheriting class n single integer. positive zero, size resulting object: number elements vector (including lists), rows matrix data frame lines function. negative, n last/first number elements x. ... arguments passed head.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/print.FielDHub.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print a FielDHub object β€” print.FielDHub","text":"object inheriting class FielDHub","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/print.FielDHub.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Print a FielDHub object β€” print.FielDHub","text":"Thiago de Paula Oliveira, thiago.paula.oliveira@alumni.usp.br [aut], Didier Murillo [aut]","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/print.FielDHub.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Print a FielDHub object β€” print.FielDHub","text":"","code":"# Example 1: Generates a CRD design with 5 treatments and 5 reps each. crd1 <- CRD(t = 5, reps = 5, plotNumber = 101, seed = 1985, locationName = \"Fargo\") crd1$infoDesign #> $numberofTreatments #> [1] 5 #> #> $treatments #> [1] \"T1\" \"T2\" \"T3\" \"T4\" \"T5\" #> #> $Reps #> [1] 5 #> #> $locationName #> [1] \"Fargo\" #> #> $seed #> [1] 1985 #> #> $id_design #> [1] 1 #> print(crd1) #> Completely Randomized Design (CRD) #> #> Information on the design parameters: #> List of 5 #> $ numberofTreatments: num 5 #> $ treatments : chr [1:5] \"T1\" \"T2\" \"T3\" \"T4\" ... #> $ Reps : num 5 #> $ locationName : chr \"Fargo\" #> $ seed : num 1985 #> #> 10 First observations of the data frame with the CRD field book: #> ID LOCATION PLOT REP TREATMENT #> 1 1 Fargo 101 3 T3 #> 2 2 Fargo 102 4 T2 #> 3 3 Fargo 103 2 T1 #> 4 4 Fargo 104 3 T5 #> 5 5 Fargo 105 2 T5 #> 6 6 Fargo 106 2 T4 #> 7 7 Fargo 107 4 T3 #> 8 8 Fargo 108 5 T4 #> 9 9 Fargo 109 1 T2 #> 10 10 Fargo 110 5 T1"},{"path":"https://didiermurillof.github.io/FielDHub/reference/print.fieldLayout.html","id":null,"dir":"Reference","previous_headings":"","what":"Print a fieldLayout plot object β€” print.fieldLayout","title":"Print a fieldLayout plot object β€” print.fieldLayout","text":"Prints plot object class fieldLayout.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/print.fieldLayout.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print a fieldLayout plot object β€” print.fieldLayout","text":"","code":"# S3 method for class 'fieldLayout' print(x, ...)"},{"path":"https://didiermurillof.github.io/FielDHub/reference/print.fieldLayout.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print a fieldLayout plot object β€” print.fieldLayout","text":"x plot object inheriting class fieldLayout. ... unused, extensibility.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/print.fieldLayout.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print a fieldLayout plot object β€” print.fieldLayout","text":"plot object inheriting class fieldLayout.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/print.fieldLayout.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Print a fieldLayout plot object β€” print.fieldLayout","text":"Didier Murillo [aut]","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/print.summary.FielDHub.html","id":null,"dir":"Reference","previous_headings":"","what":"Print the summary of a FielDHub object β€” print.summary.FielDHub","title":"Print the summary of a FielDHub object β€” print.summary.FielDHub","text":"Print summary information design parameters, data frame structure","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/print.summary.FielDHub.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print the summary of a FielDHub object β€” print.summary.FielDHub","text":"","code":"# S3 method for class 'summary.FielDHub' print(x, ...)"},{"path":"https://didiermurillof.github.io/FielDHub/reference/print.summary.FielDHub.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print the summary of a FielDHub object β€” print.summary.FielDHub","text":"x object inheriting class FielDHub ... Unused, extensibility","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/print.summary.FielDHub.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print the summary of a FielDHub object β€” print.summary.FielDHub","text":"object inheriting class FielDHub","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/print.summary.FielDHub.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Print the summary of a FielDHub object β€” print.summary.FielDHub","text":"Thiago de Paula Oliveira, thiago.paula.oliveira@alumni.usp.br [aut], Didier Murillo [aut]","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/rectangular_lattice.html","id":null,"dir":"Reference","previous_headings":"","what":"Generates a Rectangular Lattice Design. β€” rectangular_lattice","title":"Generates a Rectangular Lattice Design. β€” rectangular_lattice","text":"randomly generates rectangular lattice design across locations.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/rectangular_lattice.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generates a Rectangular Lattice Design. β€” rectangular_lattice","text":"","code":"rectangular_lattice( t = NULL, k = NULL, r = NULL, l = 1, plotNumber = 101, locationNames = NULL, seed = NULL, data = NULL )"},{"path":"https://didiermurillof.github.io/FielDHub/reference/rectangular_lattice.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generates a Rectangular Lattice Design. β€” rectangular_lattice","text":"t Number treatments. k Size incomplete blocks (number units per incomplete block). r Number blocks (full resolvable replicates). l Number locations. default l = 1. plotNumber Numeric vector starting plot number location. default plotNumber = 101. locationNames (optional) Names location. seed (optional) Real number specifies starting seed obtain reproducible designs. data (optional) Data frame label list treatments.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/rectangular_lattice.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generates a Rectangular Lattice Design. β€” rectangular_lattice","text":"list two elements. infoDesign list information design parameters. fieldBook data frame rectangular lattice design field book.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/rectangular_lattice.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Generates a Rectangular Lattice Design. β€” rectangular_lattice","text":"Edmondson., R. N. (2021). blocksdesign: Nested crossed block designs factorial unstructured treatment sets. https://CRAN.R-project.org/package=blocksdesign","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/rectangular_lattice.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Generates a Rectangular Lattice Design. β€” rectangular_lattice","text":"Didier Murillo [aut], Salvador Gezan [aut], Ana Heilman [ctb], Thomas Walk [ctb], Johan Aparicio [ctb], Richard Horsley [ctb]","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/rectangular_lattice.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generates a Rectangular Lattice Design. β€” rectangular_lattice","text":"","code":"# Example 1: Generates a rectangular lattice design with 6 full blocks, 4 units per IBlock (k) # and 20 treatments in one location. rectangularLattice1 <- rectangular_lattice(t = 20, k = 4, r = 6, l = 1, plotNumber = 101, locationNames = \"FARGO\", seed = 126) rectangularLattice1$infoDesign #> $Reps #> [1] 6 #> #> $iBlocks #> [1] 5 #> #> $NumberTreatments #> [1] 20 #> #> $NumberLocations #> [1] 1 #> #> $Locations #> [1] \"FARGO\" #> #> $seed #> [1] 126 #> #> $lambda #> [1] 0.9473684 #> #> $id_design #> [1] 11 #> head(rectangularLattice1$fieldBook,12) #> ID LOCATION PLOT REP IBLOCK UNIT ENTRY TREATMENT #> 1 1 FARGO 101 1 1 1 5 G-5 #> 2 2 FARGO 102 1 1 2 15 G-15 #> 3 3 FARGO 103 1 1 3 3 G-3 #> 4 4 FARGO 104 1 1 4 14 G-14 #> 5 5 FARGO 105 1 2 1 12 G-12 #> 6 6 FARGO 106 1 2 2 1 G-1 #> 7 7 FARGO 107 1 2 3 10 G-10 #> 8 8 FARGO 108 1 2 4 16 G-16 #> 9 9 FARGO 109 1 3 1 7 G-7 #> 10 10 FARGO 110 1 3 2 19 G-19 #> 11 11 FARGO 111 1 3 3 11 G-11 #> 12 12 FARGO 112 1 3 4 6 G-6 # Example 2: Generates a rectangular lattice design with 5 full blocks, 7 units per IBlock (k) # and 56 treatments across 2 locations. # In this case, we show how to use the option data. treatments <- paste(\"ND-\", 1:56, sep = \"\") ENTRY <- 1:56 treatment_list <- data.frame(list(ENTRY = ENTRY, TREATMENT = treatments)) head(treatment_list) #> ENTRY TREATMENT #> 1 1 ND-1 #> 2 2 ND-2 #> 3 3 ND-3 #> 4 4 ND-4 #> 5 5 ND-5 #> 6 6 ND-6 rectangularLattice2 <- rectangular_lattice(t = 56, k = 7, r = 5, l = 2, plotNumber = c(1001,2001), locationNames = c(\"Loc1\", \"Loc2\"), seed = 127, data = treatment_list) rectangularLattice2$infoDesign #> $Reps #> [1] 5 #> #> $iBlocks #> [1] 8 #> #> $NumberTreatments #> [1] 56 #> #> $NumberLocations #> [1] 2 #> #> $Locations #> [1] \"LOC1\" \"LOC2\" #> #> $seed #> [1] 127 #> #> $lambda #> [1] 0.5454545 #> #> $id_design #> [1] 11 #> head(rectangularLattice2$fieldBook,12) #> ID LOCATION PLOT REP IBLOCK UNIT ENTRY TREATMENT #> 1 1 LOC1 1001 1 1 1 43 ND-43 #> 2 2 LOC1 1002 1 1 2 6 ND-6 #> 3 3 LOC1 1003 1 1 3 5 ND-5 #> 4 4 LOC1 1004 1 1 4 27 ND-27 #> 5 5 LOC1 1005 1 1 5 54 ND-54 #> 6 6 LOC1 1006 1 1 6 41 ND-41 #> 7 7 LOC1 1007 1 1 7 26 ND-26 #> 8 8 LOC1 1008 1 2 1 24 ND-24 #> 9 9 LOC1 1009 1 2 2 51 ND-51 #> 10 10 LOC1 1010 1 2 3 21 ND-21 #> 11 11 LOC1 1011 1 2 4 20 ND-20 #> 12 12 LOC1 1012 1 2 5 11 ND-11"},{"path":"https://didiermurillof.github.io/FielDHub/reference/row_column.html","id":null,"dir":"Reference","previous_headings":"","what":"Generates a Resolvable Row-Column Design (RowColD) β€” row_column","title":"Generates a Resolvable Row-Column Design (RowColD) β€” row_column","text":"randomly generates resolvable row-column design (RowColD). design optimized rows columns blocking factors. randomization can done across multiple locations.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/row_column.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generates a Resolvable Row-Column Design (RowColD) β€” row_column","text":"","code":"row_column( t = NULL, nrows = NULL, r = NULL, l = 1, plotNumber = 101, locationNames = NULL, seed = NULL, iterations = 1000, data = NULL )"},{"path":"https://didiermurillof.github.io/FielDHub/reference/row_column.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generates a Resolvable Row-Column Design (RowColD) β€” row_column","text":"t Number treatments. nrows Number rows full resolvable replicate. r Number blocks (full resolvable replicates). l Number locations. default l = 1. plotNumber Numeric vector starting plot number location. default plotNumber = 101. locationNames (optional) Names location. seed (optional) Real number specifies starting seed obtain reproducible designs. iterations Number iterations design optimization. default iterations = 1000. data (optional) Data frame label list treatments","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/row_column.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generates a Resolvable Row-Column Design (RowColD) β€” row_column","text":"list four elements. infoDesign list information design parameters. resolvableBlocks list resolvable row columns blocks. concurrence concurrence matrix. fieldBook data frame row-column field book.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/row_column.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generates a Resolvable Row-Column Design (RowColD) β€” row_column","text":"Row-Column design FielDHub built two stages. first step constructs blocking factor Columns using Incomplete Block Units incomplete block design sets number incomplete blocks number Columns design, dimension equal number Rows. design generated, Rows used Row blocking factor optimized -Efficiency, levels within original Columns fixed. optimize Rows maintaining current optimized Columns, use heuristic algorithm swaps random treatment positions within given Column (Block) also selected random. algorithm begins calculating -Efficiency initial design, performs swap iteration, recalculates -Efficiency resulting design, compares previous one decide whether keep discard new design. iterative process repeated, default, 1,000 times.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/row_column.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Generates a Resolvable Row-Column Design (RowColD) β€” row_column","text":"Edmondson., R. N. (2021). blocksdesign: Nested crossed block designs factorial unstructured treatment sets. https://CRAN.R-project.org/package=blocksdesign","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/row_column.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Generates a Resolvable Row-Column Design (RowColD) β€” row_column","text":"Didier Murillo [aut], Salvador Gezan [aut], Ana Heilman [ctb], Thomas Walk [ctb], Johan Aparicio [ctb], Richard Horsley [ctb]","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/row_column.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generates a Resolvable Row-Column Design (RowColD) β€” row_column","text":"","code":"# Example 1: Generates a row-column design with 2 full blocks and 24 treatments # and 6 rows. This for one location. This example uses 100 iterations for the optimization # but 1000 is the default and recomended value. rowcold1 <- row_column( t = 24, nrows = 6, r = 2, l = 1, plotNumber= 101, locationNames = \"Loc1\", iterations = 100, seed = 21 ) rowcold1$infoDesign #> $rows #> [1] 6 #> #> $columns #> [1] 4 #> #> $reps #> [1] 2 #> #> $treatments #> [1] 24 #> #> $locations #> [1] 1 #> #> $location_names #> [1] \"Loc1\" #> #> $seed #> [1] 21 #> #> $id_design #> [1] 9 #> rowcold1$resolvableBlocks #> $Loc_Loc1 #> $Loc_Loc1$rep1 #> [,1] [,2] [,3] [,4] #> [1,] NA NA NA NA #> [2,] NA NA NA NA #> [3,] NA NA NA NA #> [4,] NA NA NA NA #> [5,] NA NA NA NA #> [6,] NA NA NA NA #> #> $Loc_Loc1$rep2 #> [,1] [,2] [,3] [,4] #> [1,] NA NA NA NA #> [2,] NA NA NA NA #> [3,] NA NA NA NA #> [4,] NA NA NA NA #> [5,] NA NA NA NA #> [6,] NA NA NA NA #> #> head(rowcold1$fieldBook,12) #> ID LOCATION PLOT REP ROW COLUMN ENTRY TREATMENT #> 1 1 Loc1 101 1 1 1 13 G-13 #> 7 2 Loc1 102 1 1 2 23 G-23 #> 13 3 Loc1 103 1 1 3 10 G-10 #> 19 4 Loc1 104 1 1 4 12 G-12 #> 2 5 Loc1 105 1 2 1 20 G-20 #> 8 6 Loc1 106 1 2 2 8 G-8 #> 14 7 Loc1 107 1 2 3 6 G-6 #> 20 8 Loc1 108 1 2 4 19 G-19 #> 3 9 Loc1 109 1 3 1 24 G-24 #> 9 10 Loc1 110 1 3 2 11 G-11 #> 15 11 Loc1 111 1 3 3 21 G-21 #> 21 12 Loc1 112 1 3 4 5 G-5 # Example 2: Generates a row-column design with 2 full blocks and 30 treatments # and 5 rows, for one location. This example uses 100 iterations for the optimization # but 1000 is the default and recommended value. # In this case, we show how to use the option data. treatments <- paste(\"ND-\", 1:30, sep = \"\") ENTRY <- 1:30 treatment_list <- data.frame(list(ENTRY = ENTRY, TREATMENT = treatments)) head(treatment_list) #> ENTRY TREATMENT #> 1 1 ND-1 #> 2 2 ND-2 #> 3 3 ND-3 #> 4 4 ND-4 #> 5 5 ND-5 #> 6 6 ND-6 rowcold2 <- row_column( t = 30, nrows = 5, r = 2, l = 1, plotNumber= 1001, locationNames = \"A\", seed = 15, iterations = 100, data = treatment_list ) rowcold2$infoDesign #> $rows #> [1] 5 #> #> $columns #> [1] 6 #> #> $reps #> [1] 2 #> #> $treatments #> [1] 30 #> #> $locations #> [1] 1 #> #> $location_names #> [1] \"A\" #> #> $seed #> [1] 15 #> #> $id_design #> [1] 9 #> rowcold2$resolvableBlocks #> $Loc_A #> $Loc_A$rep1 #> [,1] [,2] [,3] [,4] [,5] [,6] #> [1,] NA NA NA NA NA NA #> [2,] NA NA NA NA NA NA #> [3,] NA NA NA NA NA NA #> [4,] NA NA NA NA NA NA #> [5,] NA NA NA NA NA NA #> #> $Loc_A$rep2 #> [,1] [,2] [,3] [,4] [,5] [,6] #> [1,] NA NA NA NA NA NA #> [2,] NA NA NA NA NA NA #> [3,] NA NA NA NA NA NA #> [4,] NA NA NA NA NA NA #> [5,] NA NA NA NA NA NA #> #> head(rowcold2$fieldBook,12) #> ID LOCATION PLOT REP ROW COLUMN ENTRY TREATMENT #> 1 1 A 1001 1 1 1 5 ND-5 #> 6 2 A 1002 1 1 2 7 ND-7 #> 11 3 A 1003 1 1 3 14 ND-14 #> 16 4 A 1004 1 1 4 23 ND-23 #> 21 5 A 1005 1 1 5 9 ND-9 #> 26 6 A 1006 1 1 6 15 ND-15 #> 2 7 A 1007 1 2 1 10 ND-10 #> 7 8 A 1008 1 2 2 17 ND-17 #> 12 9 A 1009 1 2 3 13 ND-13 #> 17 10 A 1010 1 2 4 6 ND-6 #> 22 11 A 1011 1 2 5 29 ND-29 #> 27 12 A 1012 1 2 6 20 ND-20"},{"path":"https://didiermurillof.github.io/FielDHub/reference/run_app.html","id":null,"dir":"Reference","previous_headings":"","what":"Run the Shiny Application β€” run_app","title":"Run the Shiny Application β€” run_app","text":"Run Shiny Application","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/run_app.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Run the Shiny Application β€” run_app","text":"","code":"run_app(...)"},{"path":"https://didiermurillof.github.io/FielDHub/reference/run_app.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Run the Shiny Application β€” run_app","text":"... Unused, extensibility","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/run_app.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Run the Shiny Application β€” run_app","text":"shiny app object","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/sparse_allocation.html","id":null,"dir":"Reference","previous_headings":"","what":"Unreplicated designs using the sparse allocation approach β€” sparse_allocation","title":"Unreplicated designs using the sparse allocation approach β€” sparse_allocation","text":"Unreplicated designs using sparse allocation approach","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/sparse_allocation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Unreplicated designs using the sparse allocation approach β€” sparse_allocation","text":"","code":"sparse_allocation( lines, nrows, ncols, l, planter = \"serpentine\", plotNumber, copies_per_entry, checks = NULL, exptName = NULL, locationNames, sparse_list, seed, data = NULL )"},{"path":"https://didiermurillof.github.io/FielDHub/reference/sparse_allocation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Unreplicated designs using the sparse allocation approach β€” sparse_allocation","text":"lines Number genotypes, experimental lines treatments. nrows Number rows field. ncols Number columns field. l Number locations sites. default l = 1. planter Option serpentine cartesian plot arrangement. default planter = 'serpentine'. plotNumber Numeric vector starting plot number location. default plotNumber = 101. copies_per_entry Number copies per plant. design sparse copies_per_entry < l checks Number genotypes checks. exptName (optional) Name experiment. locationNames (optional) Names location. sparse_list (optional) class \"Sparse\" object generated do_optim() function. seed (optional) Real number specifies starting seed obtain reproducible designs. data (optional) Data frame 2 columns: ENTRY | NAME . ENTRY must numeric.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/sparse_allocation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Unreplicated designs using the sparse allocation approach β€” sparse_allocation","text":"list four elements. designs list location unreplicated randomization. list_locs list location list entries. allocation matrix allocation treatments. size_locations data frame one column location one row size location.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/sparse_allocation.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Unreplicated designs using the sparse allocation approach β€” sparse_allocation","text":"Edmondson, R.N. Multi-level Block Designs Comparative Experiments. JABES 25, 500–522 (2020). https://doi.org/10.1007/s13253-020-00416-0","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/sparse_allocation.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Unreplicated designs using the sparse allocation approach β€” sparse_allocation","text":"Didier Murillo [aut], Salvador Gezan [aut], Ana Heilman [ctb]","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/sparse_allocation.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Unreplicated designs using the sparse allocation approach β€” sparse_allocation","text":"","code":"sparse <- sparse_allocation( lines = 120, l = 4, copies_per_entry = 3, checks = 4, locationNames = c(\"LOC1\", \"LOC2\", \"LOC3\", \"LOC4\", \"LOC5\"), seed = 1234 )"},{"path":"https://didiermurillof.github.io/FielDHub/reference/split_families.html","id":null,"dir":"Reference","previous_headings":"","what":"Split a population of genotypes randomly into several locations. β€” split_families","title":"Split a population of genotypes randomly into several locations. β€” split_families","text":"Split population genotypes randomly several locations, aim approximatelly number replicates genotype, line treatment per location.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/split_families.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Split a population of genotypes randomly into several locations. β€” split_families","text":"","code":"split_families(l = NULL, data = NULL)"},{"path":"https://didiermurillof.github.io/FielDHub/reference/split_families.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Split a population of genotypes randomly into several locations. β€” split_families","text":"l Number locations. data Data frame entry (ENTRY) labels treatment (NAME) number individuals per family group (FAMILY).","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/split_families.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Split a population of genotypes randomly into several locations. β€” split_families","text":"list two elements. rowsEachlist table summary cases. data_locations data frame entries location","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/split_families.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Split a population of genotypes randomly into several locations. β€” split_families","text":"Didier Murillo [aut], Salvador Gezan [aut], Ana Heilman [ctb], Thomas Walk [ctb], Johan Aparicio [ctb], Richard Horsley [ctb]","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/split_families.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Split a population of genotypes randomly into several locations. β€” split_families","text":"","code":"# Example 1: Split a population of 3000 and 200 families into 8 locations. # Original dataset is been simulated. set.seed(77) N <- 2000; families <- 100 ENTRY <- 1:N NAME <- paste0(\"SB-\", 1:N) FAMILY <- vector(mode = \"numeric\", length = N) x <- 1:N for (i in x) { FAMILY[i] <- sample(1:families, size = 1, replace = TRUE) } gen.list <- data.frame(list(ENTRY = ENTRY, NAME = NAME, FAMILY = FAMILY)) head(gen.list) #> ENTRY NAME FAMILY #> 1 1 SB-1 18 #> 2 2 SB-2 45 #> 3 3 SB-3 69 #> 4 4 SB-4 57 #> 5 5 SB-5 37 #> 6 6 SB-6 29 # Now we are going to use the split_families() function. split_population <- split_families(l = 8, data = gen.list) #> Error: object 'gen.list' not found print(split_population) #> Error in eval(expr, envir, enclos): object 'split_population' not found summary(split_population) #> Error in eval(expr, envir, enclos): object 'split_population' not found head(split_population$data_locations,12) #> Error in eval(expr, envir, enclos): object 'split_population' not found"},{"path":"https://didiermurillof.github.io/FielDHub/reference/split_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Generates a Split Plot Design β€” split_plot","title":"Generates a Split Plot Design β€” split_plot","text":"randomly generates split plot design (SPD) across locations.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/split_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generates a Split Plot Design β€” split_plot","text":"","code":"split_plot( wp = NULL, sp = NULL, reps = NULL, type = 2, l = 1, plotNumber = 101, seed = NULL, locationNames = NULL, factorLabels = TRUE, data = NULL )"},{"path":"https://didiermurillof.github.io/FielDHub/reference/split_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generates a Split Plot Design β€” split_plot","text":"wp Number whole plots, integer vector. sp Number sub plots per whole plot, integer vector. reps Number blocks (full replicates). type Option CRD RCBD designs. Values type = 1 (CRD) type = 2 (RCBD). default type = 2. l Number locations. default l = 1. plotNumber Numeric vector starting plot number location. default plotNumber = 101. seed (optional) Real number specifies starting seed obtain reproducible designs. locationNames (optional) Names location. factorLabels (optional) TRUE retain levels labels original data set otherwise, numeric labels assigned. Default factorLabels =TRUE. data (optional) Data frame label list treatments.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/split_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generates a Split Plot Design β€” split_plot","text":"list two elements. infoDesign list information design parameters. fieldBook data frame split plot field book.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/split_plot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Generates a Split Plot Design β€” split_plot","text":"Federer, W. T. (1955). Experimental Design. Theory Application. New York, USA. Macmillan Company.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/split_plot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Generates a Split Plot Design β€” split_plot","text":"Didier Murillo [aut], Salvador Gezan [aut], Ana Heilman [ctb], Thomas Walk [ctb], Johan Aparicio [ctb], Richard Horsley [ctb]","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/split_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generates a Split Plot Design β€” split_plot","text":"","code":"# Example 1: Generates a split plot design SPD with 4 whole plots, 2 sub plots per whole plot, # and 4 reps in an RCBD arrangement. This in for a single location. SPDExample1 <- split_plot(wp = 4, sp = 2, reps = 5, l = 1, plotNumber = 101, seed = 14, type = 2, locationNames = \"FARGO\") SPDExample1$infoDesign #> $WholePlots #> [1] 1 2 3 4 #> #> $SubPlots #> [1] 1 2 #> #> $locationNumber #> [1] 1 #> #> $locationNames #> [1] \"FARGO\" #> #> $plotNumbers #> [1] 101 #> #> $typeDesign #> [1] \"RCBD\" #> #> $seed #> [1] 14 #> #> $id_design #> [1] 5 #> SPDExample1$layoutlocations #> [[1]] #> PLOT REP Whole-plot Sub-plot #> [1,] \"101\" \"1\" \"1\" \"1 2\" #> [2,] \"102\" \"1\" \"4\" \"2 1\" #> [3,] \"103\" \"1\" \"3\" \"2 1\" #> [4,] \"104\" \"1\" \"2\" \"2 1\" #> [5,] \"201\" \"2\" \"3\" \"1 2\" #> [6,] \"202\" \"2\" \"2\" \"2 1\" #> [7,] \"203\" \"2\" \"4\" \"2 1\" #> [8,] \"204\" \"2\" \"1\" \"2 1\" #> [9,] \"301\" \"3\" \"4\" \"2 1\" #> [10,] \"302\" \"3\" \"2\" \"1 2\" #> [11,] \"303\" \"3\" \"1\" \"2 1\" #> [12,] \"304\" \"3\" \"3\" \"2 1\" #> [13,] \"401\" \"4\" \"1\" \"2 1\" #> [14,] \"402\" \"4\" \"3\" \"2 1\" #> [15,] \"403\" \"4\" \"2\" \"2 1\" #> [16,] \"404\" \"4\" \"4\" \"1 2\" #> [17,] \"501\" \"5\" \"3\" \"1 2\" #> [18,] \"502\" \"5\" \"1\" \"2 1\" #> [19,] \"503\" \"5\" \"2\" \"2 1\" #> [20,] \"504\" \"5\" \"4\" \"2 1\" #> head(SPDExample1$fieldBook,12) #> ID LOCATION PLOT REP WHOLE_PLOT SUB_PLOT TRT_COMB #> 1 1 FARGO 101 1 1 1 1|1 #> 2 2 FARGO 101 1 1 2 1|2 #> 3 3 FARGO 102 1 4 2 4|2 #> 4 4 FARGO 102 1 4 1 4|1 #> 5 5 FARGO 103 1 3 2 3|2 #> 6 6 FARGO 103 1 3 1 3|1 #> 7 7 FARGO 104 1 2 2 2|2 #> 8 8 FARGO 104 1 2 1 2|1 #> 9 9 FARGO 201 2 3 1 3|1 #> 10 10 FARGO 201 2 3 2 3|2 #> 11 11 FARGO 202 2 2 2 2|2 #> 12 12 FARGO 202 2 2 1 2|1 # Example 2: Generates a split plot design SPD with 5 whole plots # (4 types of fungicide + one control), 10 sub plots per whole plot (10 bean varieties), # and 6 reps in an RCBD arrangement. This in 3 locations or sites. # In this case, we show how to use the option data. wp <- c(\"NFung\", paste(\"Fung\", 1:4, sep = \"\")) # Fungicides (5 Whole plots) sp <- paste(\"Beans\", 1:10, sep = \"\") # Beans varieties (10 sub plots) split_plot_Data <- data.frame(list(WHOLPLOT = c(wp, rep(NA, 5)), SUBPLOT = sp)) head(split_plot_Data, 12) #> WHOLPLOT SUBPLOT #> 1 NFung Beans1 #> 2 Fung1 Beans2 #> 3 Fung2 Beans3 #> 4 Fung3 Beans4 #> 5 Fung4 Beans5 #> 6 Beans6 #> 7 Beans7 #> 8 Beans8 #> 9 Beans9 #> 10 Beans10 SPDExample2 <- split_plot(reps = 6, l = 3, plotNumber = c(101, 1001, 2001), seed = 23, type = 2, locationNames = c(\"A\", \"B\", \"C\"), data = split_plot_Data) SPDExample2$infoDesign #> $WholePlots #> [1] \"NFung\" \"Fung1\" \"Fung2\" \"Fung3\" \"Fung4\" #> #> $SubPlots #> [1] \"Beans1\" \"Beans2\" \"Beans3\" \"Beans4\" \"Beans5\" \"Beans6\" \"Beans7\" #> [8] \"Beans8\" \"Beans9\" \"Beans10\" #> #> $locationNumber #> [1] 3 #> #> $locationNames #> [1] \"A\" \"B\" \"C\" #> #> $plotNumbers #> [1] 101 1001 2001 #> #> $typeDesign #> [1] \"RCBD\" #> #> $seed #> [1] 23 #> #> $id_design #> [1] 5 #> SPDExample2$layoutlocations #> [[1]] #> PLOT REP Whole-plot #> [1,] \"101\" \"1\" \"Fung4\" #> [2,] \"102\" \"1\" \"Fung3\" #> [3,] \"103\" \"1\" \"Fung2\" #> [4,] \"104\" \"1\" \"Fung1\" #> [5,] \"105\" \"1\" \"NFung\" #> [6,] \"201\" \"2\" \"Fung2\" #> [7,] \"202\" \"2\" \"Fung4\" #> [8,] \"203\" \"2\" \"NFung\" #> [9,] \"204\" \"2\" \"Fung1\" #> [10,] \"205\" \"2\" \"Fung3\" #> [11,] \"301\" \"3\" \"NFung\" #> [12,] \"302\" \"3\" \"Fung2\" #> [13,] \"303\" \"3\" \"Fung4\" #> [14,] \"304\" \"3\" \"Fung3\" #> [15,] \"305\" \"3\" \"Fung1\" #> [16,] \"401\" \"4\" \"Fung3\" #> [17,] \"402\" \"4\" \"Fung2\" #> [18,] \"403\" \"4\" \"Fung1\" #> [19,] \"404\" \"4\" \"Fung4\" #> [20,] \"405\" \"4\" \"NFung\" #> [21,] \"501\" \"5\" \"Fung4\" #> [22,] \"502\" \"5\" \"Fung1\" #> [23,] \"503\" \"5\" \"Fung3\" #> [24,] \"504\" \"5\" \"NFung\" #> [25,] \"505\" \"5\" \"Fung2\" #> [26,] \"601\" \"6\" \"Fung1\" #> [27,] \"602\" \"6\" \"Fung2\" #> [28,] \"603\" \"6\" \"Fung3\" #> [29,] \"604\" \"6\" \"Fung4\" #> [30,] \"605\" \"6\" \"NFung\" #> Sub-plot #> [1,] \"Beans5 Beans1 Beans2 Beans3 Beans10 Beans6 Beans7 Beans9 Beans4 Beans8\" #> [2,] \"Beans9 Beans10 Beans8 Beans5 Beans7 Beans4 Beans2 Beans6 Beans3 Beans1\" #> [3,] \"Beans7 Beans10 Beans6 Beans2 Beans8 Beans3 Beans1 Beans4 Beans9 Beans5\" #> [4,] \"Beans7 Beans9 Beans8 Beans2 Beans3 Beans1 Beans5 Beans4 Beans6 Beans10\" #> [5,] \"Beans9 Beans10 Beans4 Beans3 Beans6 Beans7 Beans1 Beans2 Beans8 Beans5\" #> [6,] \"Beans4 Beans3 Beans9 Beans10 Beans1 Beans8 Beans5 Beans7 Beans6 Beans2\" #> [7,] \"Beans8 Beans7 Beans1 Beans5 Beans2 Beans10 Beans9 Beans6 Beans4 Beans3\" #> [8,] \"Beans8 Beans4 Beans1 Beans2 Beans9 Beans6 Beans3 Beans5 Beans10 Beans7\" #> [9,] \"Beans1 Beans7 Beans5 Beans4 Beans6 Beans9 Beans2 Beans8 Beans10 Beans3\" #> [10,] \"Beans6 Beans1 Beans4 Beans2 Beans7 Beans10 Beans3 Beans8 Beans9 Beans5\" #> [11,] \"Beans5 Beans3 Beans6 Beans9 Beans4 Beans1 Beans10 Beans7 Beans2 Beans8\" #> [12,] \"Beans3 Beans7 Beans4 Beans2 Beans8 Beans6 Beans5 Beans9 Beans10 Beans1\" #> [13,] \"Beans1 Beans6 Beans7 Beans9 Beans3 Beans2 Beans4 Beans5 Beans8 Beans10\" #> [14,] \"Beans8 Beans4 Beans2 Beans6 Beans10 Beans3 Beans5 Beans1 Beans9 Beans7\" #> [15,] \"Beans3 Beans8 Beans5 Beans1 Beans7 Beans10 Beans6 Beans2 Beans9 Beans4\" #> [16,] \"Beans4 Beans7 Beans5 Beans8 Beans9 Beans2 Beans10 Beans6 Beans1 Beans3\" #> [17,] \"Beans8 Beans9 Beans2 Beans1 Beans7 Beans6 Beans5 Beans10 Beans4 Beans3\" #> [18,] \"Beans9 Beans8 Beans2 Beans5 Beans1 Beans6 Beans10 Beans7 Beans4 Beans3\" #> [19,] \"Beans10 Beans3 Beans6 Beans1 Beans5 Beans8 Beans7 Beans2 Beans4 Beans9\" #> [20,] \"Beans6 Beans8 Beans10 Beans1 Beans7 Beans3 Beans5 Beans4 Beans2 Beans9\" #> [21,] \"Beans8 Beans7 Beans9 Beans6 Beans1 Beans5 Beans2 Beans3 Beans10 Beans4\" #> [22,] \"Beans3 Beans9 Beans8 Beans4 Beans1 Beans7 Beans10 Beans6 Beans2 Beans5\" #> [23,] \"Beans2 Beans5 Beans10 Beans1 Beans7 Beans6 Beans9 Beans4 Beans8 Beans3\" #> [24,] \"Beans8 Beans5 Beans7 Beans1 Beans9 Beans6 Beans2 Beans4 Beans3 Beans10\" #> [25,] \"Beans7 Beans8 Beans10 Beans4 Beans1 Beans9 Beans3 Beans2 Beans5 Beans6\" #> [26,] \"Beans3 Beans8 Beans4 Beans9 Beans2 Beans6 Beans1 Beans7 Beans10 Beans5\" #> [27,] \"Beans4 Beans10 Beans1 Beans8 Beans3 Beans9 Beans7 Beans5 Beans6 Beans2\" #> [28,] \"Beans5 Beans3 Beans6 Beans4 Beans2 Beans10 Beans8 Beans1 Beans9 Beans7\" #> [29,] \"Beans3 Beans7 Beans2 Beans5 Beans1 Beans9 Beans4 Beans10 Beans8 Beans6\" #> [30,] \"Beans8 Beans6 Beans2 Beans5 Beans9 Beans10 Beans1 Beans3 Beans4 Beans7\" #> #> [[2]] #> PLOT REP Whole-plot #> [1,] \"1001\" \"1\" \"Fung1\" #> [2,] \"1002\" \"1\" \"Fung3\" #> [3,] \"1003\" \"1\" \"NFung\" #> [4,] \"1004\" \"1\" \"Fung2\" #> [5,] \"1005\" \"1\" \"Fung4\" #> [6,] \"1101\" \"2\" \"Fung3\" #> [7,] \"1102\" \"2\" \"Fung2\" #> [8,] \"1103\" \"2\" \"Fung4\" #> [9,] \"1104\" \"2\" \"Fung1\" #> [10,] \"1105\" \"2\" \"NFung\" #> [11,] \"1201\" \"3\" \"NFung\" #> [12,] \"1202\" \"3\" \"Fung2\" #> [13,] \"1203\" \"3\" \"Fung1\" #> [14,] \"1204\" \"3\" \"Fung4\" #> [15,] \"1205\" \"3\" \"Fung3\" #> [16,] \"1301\" \"4\" \"Fung3\" #> [17,] \"1302\" \"4\" \"NFung\" #> [18,] \"1303\" \"4\" \"Fung2\" #> [19,] \"1304\" \"4\" \"Fung4\" #> [20,] \"1305\" \"4\" \"Fung1\" #> [21,] \"1401\" \"5\" \"Fung2\" #> [22,] \"1402\" \"5\" \"NFung\" #> [23,] \"1403\" \"5\" \"Fung1\" #> [24,] \"1404\" \"5\" \"Fung4\" #> [25,] \"1405\" \"5\" \"Fung3\" #> [26,] \"1501\" \"6\" \"Fung2\" #> [27,] \"1502\" \"6\" \"Fung1\" #> [28,] \"1503\" \"6\" \"NFung\" #> [29,] \"1504\" \"6\" \"Fung4\" #> [30,] \"1505\" \"6\" \"Fung3\" #> Sub-plot #> [1,] \"Beans3 Beans6 Beans8 Beans9 Beans4 Beans5 Beans7 Beans2 Beans1 Beans10\" #> [2,] \"Beans2 Beans4 Beans9 Beans10 Beans8 Beans3 Beans5 Beans6 Beans1 Beans7\" #> [3,] \"Beans3 Beans7 Beans1 Beans6 Beans5 Beans2 Beans4 Beans10 Beans8 Beans9\" #> [4,] \"Beans3 Beans5 Beans7 Beans6 Beans4 Beans10 Beans2 Beans9 Beans8 Beans1\" #> [5,] \"Beans4 Beans9 Beans8 Beans3 Beans6 Beans7 Beans5 Beans1 Beans2 Beans10\" #> [6,] \"Beans6 Beans3 Beans5 Beans2 Beans7 Beans10 Beans9 Beans1 Beans8 Beans4\" #> [7,] \"Beans8 Beans5 Beans6 Beans7 Beans10 Beans2 Beans3 Beans9 Beans4 Beans1\" #> [8,] \"Beans3 Beans1 Beans10 Beans4 Beans7 Beans9 Beans5 Beans2 Beans8 Beans6\" #> [9,] \"Beans7 Beans3 Beans9 Beans10 Beans1 Beans5 Beans6 Beans4 Beans8 Beans2\" #> [10,] \"Beans10 Beans1 Beans5 Beans9 Beans6 Beans3 Beans8 Beans7 Beans4 Beans2\" #> [11,] \"Beans6 Beans7 Beans8 Beans3 Beans5 Beans4 Beans2 Beans1 Beans9 Beans10\" #> [12,] \"Beans3 Beans9 Beans8 Beans5 Beans2 Beans1 Beans4 Beans6 Beans10 Beans7\" #> [13,] \"Beans2 Beans5 Beans9 Beans1 Beans8 Beans3 Beans4 Beans6 Beans10 Beans7\" #> [14,] \"Beans10 Beans7 Beans9 Beans8 Beans5 Beans1 Beans4 Beans3 Beans2 Beans6\" #> [15,] \"Beans1 Beans8 Beans2 Beans3 Beans7 Beans6 Beans5 Beans10 Beans4 Beans9\" #> [16,] \"Beans1 Beans4 Beans3 Beans9 Beans10 Beans5 Beans6 Beans7 Beans2 Beans8\" #> [17,] \"Beans1 Beans8 Beans6 Beans9 Beans7 Beans2 Beans3 Beans5 Beans10 Beans4\" #> [18,] \"Beans2 Beans3 Beans1 Beans8 Beans7 Beans6 Beans4 Beans9 Beans5 Beans10\" #> [19,] \"Beans9 Beans1 Beans10 Beans8 Beans7 Beans3 Beans5 Beans6 Beans4 Beans2\" #> [20,] \"Beans2 Beans1 Beans3 Beans7 Beans4 Beans10 Beans8 Beans6 Beans9 Beans5\" #> [21,] \"Beans10 Beans9 Beans6 Beans7 Beans4 Beans3 Beans5 Beans8 Beans1 Beans2\" #> [22,] \"Beans3 Beans10 Beans4 Beans7 Beans1 Beans8 Beans2 Beans9 Beans5 Beans6\" #> [23,] \"Beans8 Beans7 Beans2 Beans3 Beans10 Beans6 Beans5 Beans4 Beans1 Beans9\" #> [24,] \"Beans3 Beans10 Beans5 Beans8 Beans9 Beans4 Beans2 Beans1 Beans7 Beans6\" #> [25,] \"Beans2 Beans8 Beans4 Beans1 Beans5 Beans6 Beans7 Beans10 Beans3 Beans9\" #> [26,] \"Beans10 Beans1 Beans6 Beans2 Beans9 Beans8 Beans3 Beans5 Beans7 Beans4\" #> [27,] \"Beans4 Beans8 Beans7 Beans5 Beans10 Beans9 Beans2 Beans3 Beans1 Beans6\" #> [28,] \"Beans7 Beans9 Beans6 Beans5 Beans1 Beans8 Beans3 Beans10 Beans4 Beans2\" #> [29,] \"Beans3 Beans9 Beans8 Beans1 Beans7 Beans10 Beans6 Beans2 Beans4 Beans5\" #> [30,] \"Beans3 Beans2 Beans1 Beans8 Beans9 Beans6 Beans4 Beans7 Beans10 Beans5\" #> #> [[3]] #> PLOT REP Whole-plot #> [1,] \"2001\" \"1\" \"NFung\" #> [2,] \"2002\" \"1\" \"Fung2\" #> [3,] \"2003\" \"1\" \"Fung1\" #> [4,] \"2004\" \"1\" \"Fung4\" #> [5,] \"2005\" \"1\" \"Fung3\" #> [6,] \"2101\" \"2\" \"Fung1\" #> [7,] \"2102\" \"2\" \"Fung2\" #> [8,] \"2103\" \"2\" \"Fung4\" #> [9,] \"2104\" \"2\" \"NFung\" #> [10,] \"2105\" \"2\" \"Fung3\" #> [11,] \"2201\" \"3\" \"Fung3\" #> [12,] \"2202\" \"3\" \"NFung\" #> [13,] \"2203\" \"3\" \"Fung4\" #> [14,] \"2204\" \"3\" \"Fung2\" #> [15,] \"2205\" \"3\" \"Fung1\" #> [16,] \"2301\" \"4\" \"Fung3\" #> [17,] \"2302\" \"4\" \"Fung4\" #> [18,] \"2303\" \"4\" \"Fung1\" #> [19,] \"2304\" \"4\" \"Fung2\" #> [20,] \"2305\" \"4\" \"NFung\" #> [21,] \"2401\" \"5\" \"Fung3\" #> [22,] \"2402\" \"5\" \"Fung2\" #> [23,] \"2403\" \"5\" \"Fung1\" #> [24,] \"2404\" \"5\" \"Fung4\" #> [25,] \"2405\" \"5\" \"NFung\" #> [26,] \"2501\" \"6\" \"Fung1\" #> [27,] \"2502\" \"6\" \"Fung3\" #> [28,] \"2503\" \"6\" \"Fung2\" #> [29,] \"2504\" \"6\" \"Fung4\" #> [30,] \"2505\" \"6\" \"NFung\" #> Sub-plot #> [1,] \"Beans3 Beans2 Beans6 Beans4 Beans5 Beans10 Beans7 Beans1 Beans8 Beans9\" #> [2,] \"Beans5 Beans4 Beans9 Beans1 Beans6 Beans2 Beans10 Beans7 Beans8 Beans3\" #> [3,] \"Beans4 Beans7 Beans6 Beans1 Beans2 Beans10 Beans9 Beans3 Beans8 Beans5\" #> [4,] \"Beans9 Beans8 Beans3 Beans6 Beans1 Beans7 Beans10 Beans5 Beans2 Beans4\" #> [5,] \"Beans7 Beans10 Beans4 Beans8 Beans2 Beans5 Beans9 Beans1 Beans3 Beans6\" #> [6,] \"Beans8 Beans1 Beans3 Beans10 Beans6 Beans4 Beans9 Beans5 Beans7 Beans2\" #> [7,] \"Beans3 Beans6 Beans1 Beans5 Beans7 Beans10 Beans9 Beans4 Beans8 Beans2\" #> [8,] \"Beans4 Beans2 Beans7 Beans1 Beans10 Beans9 Beans6 Beans5 Beans3 Beans8\" #> [9,] \"Beans2 Beans7 Beans9 Beans3 Beans4 Beans1 Beans5 Beans6 Beans8 Beans10\" #> [10,] \"Beans9 Beans2 Beans4 Beans5 Beans6 Beans3 Beans1 Beans8 Beans10 Beans7\" #> [11,] \"Beans6 Beans10 Beans1 Beans7 Beans2 Beans9 Beans4 Beans5 Beans8 Beans3\" #> [12,] \"Beans5 Beans3 Beans10 Beans9 Beans4 Beans2 Beans7 Beans6 Beans1 Beans8\" #> [13,] \"Beans1 Beans2 Beans7 Beans8 Beans5 Beans3 Beans10 Beans6 Beans9 Beans4\" #> [14,] \"Beans5 Beans8 Beans1 Beans3 Beans10 Beans6 Beans2 Beans9 Beans4 Beans7\" #> [15,] \"Beans5 Beans8 Beans3 Beans4 Beans9 Beans1 Beans10 Beans7 Beans2 Beans6\" #> [16,] \"Beans9 Beans5 Beans8 Beans1 Beans3 Beans7 Beans2 Beans6 Beans10 Beans4\" #> [17,] \"Beans2 Beans5 Beans3 Beans9 Beans6 Beans7 Beans1 Beans4 Beans8 Beans10\" #> [18,] \"Beans3 Beans10 Beans1 Beans8 Beans2 Beans6 Beans9 Beans4 Beans5 Beans7\" #> [19,] \"Beans9 Beans10 Beans1 Beans7 Beans3 Beans6 Beans5 Beans4 Beans2 Beans8\" #> [20,] \"Beans9 Beans1 Beans4 Beans3 Beans6 Beans2 Beans5 Beans10 Beans8 Beans7\" #> [21,] \"Beans1 Beans9 Beans4 Beans8 Beans2 Beans5 Beans7 Beans6 Beans3 Beans10\" #> [22,] \"Beans4 Beans5 Beans10 Beans7 Beans6 Beans1 Beans2 Beans9 Beans8 Beans3\" #> [23,] \"Beans6 Beans3 Beans10 Beans8 Beans1 Beans9 Beans2 Beans4 Beans5 Beans7\" #> [24,] \"Beans6 Beans7 Beans8 Beans4 Beans3 Beans1 Beans9 Beans2 Beans5 Beans10\" #> [25,] \"Beans3 Beans9 Beans8 Beans7 Beans1 Beans5 Beans10 Beans2 Beans4 Beans6\" #> [26,] \"Beans8 Beans5 Beans7 Beans10 Beans1 Beans3 Beans2 Beans6 Beans4 Beans9\" #> [27,] \"Beans9 Beans4 Beans10 Beans7 Beans1 Beans6 Beans8 Beans5 Beans3 Beans2\" #> [28,] \"Beans7 Beans8 Beans1 Beans10 Beans3 Beans4 Beans2 Beans6 Beans9 Beans5\" #> [29,] \"Beans3 Beans5 Beans4 Beans2 Beans7 Beans10 Beans9 Beans8 Beans1 Beans6\" #> [30,] \"Beans3 Beans6 Beans2 Beans9 Beans1 Beans4 Beans8 Beans7 Beans5 Beans10\" #> head(SPDExample2$fieldBook,12) #> ID LOCATION PLOT REP WHOLE_PLOT SUB_PLOT TRT_COMB #> 1 1 A 101 1 Fung4 Beans5 Fung4|Beans5 #> 2 2 A 101 1 Fung4 Beans1 Fung4|Beans1 #> 3 3 A 101 1 Fung4 Beans2 Fung4|Beans2 #> 4 4 A 101 1 Fung4 Beans3 Fung4|Beans3 #> 5 5 A 101 1 Fung4 Beans10 Fung4|Beans10 #> 6 6 A 101 1 Fung4 Beans6 Fung4|Beans6 #> 7 7 A 101 1 Fung4 Beans7 Fung4|Beans7 #> 8 8 A 101 1 Fung4 Beans9 Fung4|Beans9 #> 9 9 A 101 1 Fung4 Beans4 Fung4|Beans4 #> 10 10 A 101 1 Fung4 Beans8 Fung4|Beans8 #> 11 11 A 102 1 Fung3 Beans9 Fung3|Beans9 #> 12 12 A 102 1 Fung3 Beans10 Fung3|Beans10"},{"path":"https://didiermurillof.github.io/FielDHub/reference/split_split_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Generates a Split Split Plot Design β€” split_split_plot","title":"Generates a Split Split Plot Design β€” split_split_plot","text":"randomly generates split split plot design (SSPD) across locations.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/split_split_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generates a Split Split Plot Design β€” split_split_plot","text":"","code":"split_split_plot( wp = NULL, sp = NULL, ssp = NULL, reps = NULL, type = 2, l = 1, plotNumber = 101, seed = NULL, locationNames = NULL, factorLabels = TRUE, data = NULL )"},{"path":"https://didiermurillof.github.io/FielDHub/reference/split_split_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generates a Split Split Plot Design β€” split_split_plot","text":"wp Number whole plots, integer vector. sp Number sub plots per whole plot, integer vector. ssp Number sub-sub plots, integer vector. reps Number blocks (full replicates). type Option CRD RCBD designs. Values type = 1 (CRD) type = 2 (RCBD). default type = 2. l Number locations. default l = 1. plotNumber Numeric vector starting plot number location. default plotNumber = 101. seed (optional) Real number specifies starting seed obtain reproducible designs. locationNames (optional) Names location. factorLabels (optional) TRUE retain levels labels original data set otherwise, numeric labels assigned. Default factorLabels =TRUE. data (optional) Data frame label list treatments.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/split_split_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generates a Split Split Plot Design β€” split_split_plot","text":"list two elements. infoDesign list information design parameters. fieldBook data frame split split plot field book.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/split_split_plot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Generates a Split Split Plot Design β€” split_split_plot","text":"Federer, W. T. (1955). Experimental Design. Theory Application. New York, USA. Macmillan Company.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/split_split_plot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Generates a Split Split Plot Design β€” split_split_plot","text":"Didier Murillo [aut], Salvador Gezan [aut], Ana Heilman [ctb], Thomas Walk [ctb], Johan Aparicio [ctb], Richard Horsley [ctb]","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/split_split_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generates a Split Split Plot Design β€” split_split_plot","text":"","code":"# Example 1: Generates a split split plot design SSPD with 5 whole plots, 2 sub-plots, # 3 sub-sub plots, and 3 reps in an RCBD arrangement. This is for one location. SSPD1 <- split_split_plot(wp = 4, sp = 2, ssp = 3, reps = 5, l = 1, plotNumber = 101, seed = 23, type = 2, locationNames = \"FARGO\") SSPD1$infoDesign #> $Whole.Plots #> [1] 1 2 3 4 #> #> $Sub.Plots #> [1] 1 2 #> #> $Sub.Sub.Plots #> [1] 1 2 3 #> #> $Locations #> [1] 1 #> #> $typeDesign #> [1] \"RCBD\" #> #> $seed #> [1] 23 #> #> $id_design #> [1] 6 #> head(SSPD1$fieldBook,12) #> ID LOCATION PLOT REP WHOLE_PLOT SUB_PLOT SUB_SUB_PLOT TRT_COMB #> 1 1 FARGO 101 1 1 2 2 1|2|2 #> 2 2 FARGO 101 1 1 2 1 1|2|1 #> 3 3 FARGO 101 1 1 2 3 1|2|3 #> 4 4 FARGO 101 1 1 1 2 1|1|2 #> 5 5 FARGO 101 1 1 1 1 1|1|1 #> 6 6 FARGO 101 1 1 1 3 1|1|3 #> 7 7 FARGO 102 1 3 1 2 3|1|2 #> 8 8 FARGO 102 1 3 1 1 3|1|1 #> 9 9 FARGO 102 1 3 1 3 3|1|3 #> 10 10 FARGO 102 1 3 2 2 3|2|2 #> 11 11 FARGO 102 1 3 2 1 3|2|1 #> 12 12 FARGO 102 1 3 2 3 3|2|3 # Example 2: Generates a split split plot design SSPD with 2 whole plost # (Irrigation, No irrigation), 5 sub plots (4 types of fungicide + one control), and # 10 sub-sub plots (Ten varieties of beans), and 4 reps in an RCBD arrangement. # This is for 3 locations. In this case, we show how to use the option data. wp <- paste(\"IRR_\", c(\"NO\", \"Yes\"), sep = \"\") #Irrigation (2 Whole plots) sp <- c(\"NFung\", paste(\"Fung\", 1:4, sep = \"\")) #Fungicides (5 Sub plots) ssp <- paste(\"Beans\", 1:10, sep = \"\") #Beans varieties (10 Sub-sub plots) split_split_plot_Data <- data.frame(list(WHOLPLOT = c(wp, rep(NA, 8)), SUBPLOT = c(sp, rep(NA, 5)), SUB_SUBPLOTS = ssp)) head(split_split_plot_Data, 10) #> WHOLPLOT SUBPLOT SUB_SUBPLOTS #> 1 IRR_NO NFung Beans1 #> 2 IRR_Yes Fung1 Beans2 #> 3 Fung2 Beans3 #> 4 Fung3 Beans4 #> 5 Fung4 Beans5 #> 6 Beans6 #> 7 Beans7 #> 8 Beans8 #> 9 Beans9 #> 10 Beans10 SSPD2 <- split_split_plot(reps = 4, l = 3, plotNumber = c(101, 1001, 2001), seed = 23, type = 2, locationNames = c(\"A\", \"B\", \"C\"), data = split_split_plot_Data) SSPD2$infoDesign #> $Whole.Plots #> [1] \"IRR_NO\" \"IRR_Yes\" #> #> $Sub.Plots #> [1] \"NFung\" \"Fung1\" \"Fung2\" \"Fung3\" \"Fung4\" #> #> $Sub.Sub.Plots #> [1] \"Beans1\" \"Beans2\" \"Beans3\" \"Beans4\" \"Beans5\" \"Beans6\" \"Beans7\" #> [8] \"Beans8\" \"Beans9\" \"Beans10\" #> #> $Locations #> [1] 3 #> #> $typeDesign #> [1] \"RCBD\" #> #> $seed #> [1] 23 #> #> $id_design #> [1] 6 #> head(SSPD2$fieldBook,12) #> ID LOCATION PLOT REP WHOLE_PLOT SUB_PLOT SUB_SUB_PLOT TRT_COMB #> 1 1 A 101 1 IRR_NO Fung3 Beans3 IRR_NO|Fung3|Beans3 #> 2 2 A 101 1 IRR_NO Fung3 Beans9 IRR_NO|Fung3|Beans9 #> 3 3 A 101 1 IRR_NO Fung3 Beans10 IRR_NO|Fung3|Beans10 #> 4 4 A 101 1 IRR_NO Fung3 Beans7 IRR_NO|Fung3|Beans7 #> 5 5 A 101 1 IRR_NO Fung3 Beans8 IRR_NO|Fung3|Beans8 #> 6 6 A 101 1 IRR_NO Fung3 Beans5 IRR_NO|Fung3|Beans5 #> 7 7 A 101 1 IRR_NO Fung3 Beans2 IRR_NO|Fung3|Beans2 #> 8 8 A 101 1 IRR_NO Fung3 Beans6 IRR_NO|Fung3|Beans6 #> 9 9 A 101 1 IRR_NO Fung3 Beans4 IRR_NO|Fung3|Beans4 #> 10 10 A 101 1 IRR_NO Fung3 Beans1 IRR_NO|Fung3|Beans1 #> 11 11 A 101 1 IRR_NO Fung1 Beans9 IRR_NO|Fung1|Beans9 #> 12 12 A 101 1 IRR_NO Fung1 Beans2 IRR_NO|Fung1|Beans2"},{"path":"https://didiermurillof.github.io/FielDHub/reference/square_lattice.html","id":null,"dir":"Reference","previous_headings":"","what":"Generates a Square Lattice Design. β€” square_lattice","title":"Generates a Square Lattice Design. β€” square_lattice","text":"randomly generates square lattice design across locations.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/square_lattice.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generates a Square Lattice Design. β€” square_lattice","text":"","code":"square_lattice( t = NULL, k = NULL, r = NULL, l = 1, plotNumber = 101, locationNames = NULL, seed = NULL, data = NULL )"},{"path":"https://didiermurillof.github.io/FielDHub/reference/square_lattice.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generates a Square Lattice Design. β€” square_lattice","text":"t Number treatments. k Size incomplete blocks (number units per incomplete block). r Number blocks (full resolvable replicates). l Number locations. default l = 1. plotNumber Numeric vector starting plot number location. default plotNumber = 101. locationNames (optional) Names location. seed (optional) Real number specifies starting seed obtain reproducible designs. data (optional) Data frame label list treatments.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/square_lattice.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generates a Square Lattice Design. β€” square_lattice","text":"list two elements. infoDesign list information design parameters. fieldBook data frame square lattice design field book.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/square_lattice.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Generates a Square Lattice Design. β€” square_lattice","text":"Edmondson., R. N. (2021). blocksdesign: Nested crossed block designs factorial unstructured treatment sets. https://CRAN.R-project.org/package=blocksdesign","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/square_lattice.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Generates a Square Lattice Design. β€” square_lattice","text":"Didier Murillo [aut], Salvador Gezan [aut], Ana Heilman [ctb], Thomas Walk [ctb], Johan Aparicio [ctb], Richard Horsley [ctb]","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/square_lattice.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generates a Square Lattice Design. β€” square_lattice","text":"","code":"# Example 1: Generates a square lattice design with 5 full blocks, 8 units per IBlock, # 8 IBlocks for a square number of treatmens of 64 in two locations. squareLattice1 <- square_lattice(t = 64, k = 8, r = 5, l = 2, plotNumber = c(1001, 2001), locationNames = c(\"FARGO\", \"MINOT\"), seed = 1986) squareLattice1$infoDesign #> $Reps #> [1] 5 #> #> $IBlocks #> [1] 8 #> #> $NumberTreatments #> [1] 64 #> #> $NumberLocations #> [1] 2 #> #> $Locations #> [1] \"FARGO\" \"MINOT\" #> #> $seed #> [1] 1986 #> #> $lambda #> [1] 0.5555556 #> #> $id_design #> [1] 10 #> head(squareLattice1$fieldBook,12) #> ID LOCATION PLOT REP IBLOCK UNIT ENTRY TREATMENT #> 1 1 FARGO 1001 1 1 1 43 G-43 #> 2 2 FARGO 1002 1 1 2 49 G-49 #> 3 3 FARGO 1003 1 1 3 35 G-35 #> 4 4 FARGO 1004 1 1 4 15 G-15 #> 5 5 FARGO 1005 1 1 5 45 G-45 #> 6 6 FARGO 1006 1 1 6 42 G-42 #> 7 7 FARGO 1007 1 1 7 40 G-40 #> 8 8 FARGO 1008 1 1 8 10 G-10 #> 9 9 FARGO 1009 1 2 1 61 G-61 #> 10 10 FARGO 1010 1 2 2 21 G-21 #> 11 11 FARGO 1011 1 2 3 62 G-62 #> 12 12 FARGO 1012 1 2 4 34 G-34 # Example 2: Generates a square lattice design with 3 full blocks, 7 units per IBlock, # 7 IBlocks for a square number of treatmens of 49 in one location. # In this case, we show how to use the option data. treatments <- paste(\"G\", 1:49, sep = \"\") ENTRY <- 1:49 treatment_list <- data.frame(list(ENTRY = ENTRY, TREATMENT = treatments)) head(treatment_list) #> ENTRY TREATMENT #> 1 1 G1 #> 2 2 G2 #> 3 3 G3 #> 4 4 G4 #> 5 5 G5 #> 6 6 G6 squareLattice2 <- square_lattice(t = 49, k = 7, r = 3, l = 1, plotNumber = 1001, locationNames = \"CASSELTON\", seed = 1986, data = treatment_list) squareLattice2$infoDesign #> $Reps #> [1] 3 #> #> $IBlocks #> [1] 7 #> #> $NumberTreatments #> [1] 49 #> #> $NumberLocations #> [1] 1 #> #> $Locations #> [1] \"CASSELTON\" #> #> $seed #> [1] 1986 #> #> $lambda #> [1] 0.375 #> #> $id_design #> [1] 10 #> head(squareLattice2$fieldBook,12) #> ID LOCATION PLOT REP IBLOCK UNIT ENTRY TREATMENT #> 1 1 CASSELTON 1001 1 1 1 27 G27 #> 2 2 CASSELTON 1002 1 1 2 30 G30 #> 3 3 CASSELTON 1003 1 1 3 42 G42 #> 4 4 CASSELTON 1004 1 1 4 1 G1 #> 5 5 CASSELTON 1005 1 1 5 20 G20 #> 6 6 CASSELTON 1006 1 1 6 26 G26 #> 7 7 CASSELTON 1007 1 1 7 48 G48 #> 8 8 CASSELTON 1008 1 2 1 49 G49 #> 9 9 CASSELTON 1009 1 2 2 29 G29 #> 10 10 CASSELTON 1010 1 2 3 24 G24 #> 11 11 CASSELTON 1011 1 2 4 34 G34 #> 12 12 CASSELTON 1012 1 2 5 47 G47"},{"path":"https://didiermurillof.github.io/FielDHub/reference/strip_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Strip Plot Design β€” strip_plot","title":"Strip Plot Design β€” strip_plot","text":"randomly generates strip plot design across locations.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/strip_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Strip Plot Design β€” strip_plot","text":"","code":"strip_plot( Hplots = NULL, Vplots = NULL, b = 1, l = 1, plotNumber = NULL, planter = \"serpentine\", locationNames = NULL, seed = NULL, factorLabels = TRUE, data = NULL )"},{"path":"https://didiermurillof.github.io/FielDHub/reference/strip_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Strip Plot Design β€” strip_plot","text":"Hplots Number horizontal factors, integer vector. Vplots Number vertical factors, integer vector. b Number blocks (full replicates). l Number locations. default l = 1. plotNumber Numeric vector starting plot number location. default plotNumber = 101. planter Option serpentine cartesian arrangement. default planter = 'serpentine'. locationNames (optional) Names location. seed (optional) Real number specifies starting seed obtain reproducible designs. factorLabels (optional) TRUE retain levels labels original data set otherwise, numeric labels assigned. Default factorLabels =TRUE. data (optional) data frame labels vertical hirizontal plots.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/strip_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Strip Plot Design β€” strip_plot","text":"list four elements. infoDesign list information design parameters. stripsBlockLoc list strip blocks location. plotLayouts list layout plot numbers location. fieldBook data frame strip plot field book.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/strip_plot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Strip Plot Design β€” strip_plot","text":"Federer, W. T. (1955). Experimental Design. Theory Application. New York, USA. Macmillan Company.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/strip_plot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Strip Plot Design β€” strip_plot","text":"Didier Murillo [aut], Salvador Gezan [aut], Ana Heilman [ctb], Thomas Walk [ctb], Johan Aparicio [ctb], Richard Horsley [ctb]","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/strip_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Strip Plot Design β€” strip_plot","text":"","code":"# Example 1: Generates a strip plot design with 5 vertical strips and 4 horizontal strips, # with 3 reps in one location. H <- paste(\"H\", 1:4, sep = \"\") V <- paste(\"V\", 1:5, sep = \"\") strip1 <- strip_plot(Hplots = H, Vplots = V, b = 3, l = 1, plotNumber = 101, planter = \"serpentine\", locationNames = \"A\", seed = 333) strip1$infoDesign #> $Hplots #> [1] 4 #> #> $Vplots #> [1] 5 #> #> $blocks #> [1] 3 #> #> $numberLocations #> [1] 1 #> #> $nameLocations #> [1] \"A\" #> #> $seed #> [1] 333 #> #> $id_design #> [1] 7 #> strip1$stripsBlockLoc #> $Loc_A #> $Loc_A$rep1 #> V4 V2 V5 V1 V3 #> H2 \"H2|V4\" \"H2|V2\" \"H2|V5\" \"H2|V1\" \"H2|V3\" #> H1 \"H1|V4\" \"H1|V2\" \"H1|V5\" \"H1|V1\" \"H1|V3\" #> H3 \"H3|V4\" \"H3|V2\" \"H3|V5\" \"H3|V1\" \"H3|V3\" #> H4 \"H4|V4\" \"H4|V2\" \"H4|V5\" \"H4|V1\" \"H4|V3\" #> #> $Loc_A$rep2 #> V1 V3 V4 V2 V5 #> H3 \"H3|V1\" \"H3|V3\" \"H3|V4\" \"H3|V2\" \"H3|V5\" #> H4 \"H4|V1\" \"H4|V3\" \"H4|V4\" \"H4|V2\" \"H4|V5\" #> H2 \"H2|V1\" \"H2|V3\" \"H2|V4\" \"H2|V2\" \"H2|V5\" #> H1 \"H1|V1\" \"H1|V3\" \"H1|V4\" \"H1|V2\" \"H1|V5\" #> #> $Loc_A$rep3 #> V3 V1 V2 V4 V5 #> H2 \"H2|V3\" \"H2|V1\" \"H2|V2\" \"H2|V4\" \"H2|V5\" #> H1 \"H1|V3\" \"H1|V1\" \"H1|V2\" \"H1|V4\" \"H1|V5\" #> H4 \"H4|V3\" \"H4|V1\" \"H4|V2\" \"H4|V4\" \"H4|V5\" #> H3 \"H3|V3\" \"H3|V1\" \"H3|V2\" \"H3|V4\" \"H3|V5\" #> #> strip1$plotLayouts #> $Loc_A #> $Loc_A$rep1 #> [,1] [,2] [,3] [,4] [,5] #> [1,] 101 102 103 104 105 #> [2,] 110 109 108 107 106 #> [3,] 111 112 113 114 115 #> [4,] 120 119 118 117 116 #> #> $Loc_A$rep2 #> [,1] [,2] [,3] [,4] [,5] #> [1,] 201 202 203 204 205 #> [2,] 210 209 208 207 206 #> [3,] 211 212 213 214 215 #> [4,] 220 219 218 217 216 #> #> $Loc_A$rep3 #> [,1] [,2] [,3] [,4] [,5] #> [1,] 301 302 303 304 305 #> [2,] 310 309 308 307 306 #> [3,] 311 312 313 314 315 #> [4,] 320 319 318 317 316 #> #> head(strip1$fieldBook,12) #> ID LOCATION PLOT REP HSTRIP VSTRIP TRT_COMB #> 1 1 A 101 1 H2 V4 H2|V4 #> 2 2 A 102 1 H2 V2 H2|V2 #> 3 3 A 103 1 H2 V5 H2|V5 #> 4 4 A 104 1 H2 V1 H2|V1 #> 5 5 A 105 1 H2 V3 H2|V3 #> 6 6 A 110 1 H1 V4 H1|V4 #> 7 7 A 109 1 H1 V2 H1|V2 #> 8 8 A 108 1 H1 V5 H1|V5 #> 9 9 A 107 1 H1 V1 H1|V1 #> 10 10 A 106 1 H1 V3 H1|V3 #> 11 11 A 111 1 H3 V4 H3|V4 #> 12 12 A 112 1 H3 V2 H3|V2 # Example 2: Generates a strip plot design with 5 vertical strips and 5 horizontal strips, # with 6 reps across to 3 locations. In this case, we show how to use the option data. Hplots <- LETTERS[1:5] Vplots <- LETTERS[1:4] strip_data <- data.frame(list(HPLOTS = Hplots, VPLOTS = c(Vplots, NA))) head(strip_data) #> HPLOTS VPLOTS #> 1 A A #> 2 B B #> 3 C C #> 4 D D #> 5 E strip2 <- strip_plot(Hplots = 5, Vplots = 5, b = 6, l = 3, plotNumber = c(101,1001,2001), planter = \"cartesian\", locationNames = c(\"A\", \"B\", \"C\"), seed = 222, data = strip_data) strip2$infoDesign #> $Hplots #> [1] 5 #> #> $Vplots #> [1] 4 #> #> $blocks #> [1] 6 #> #> $numberLocations #> [1] 3 #> #> $nameLocations #> [1] \"A\" \"B\" \"C\" #> #> $seed #> [1] 222 #> #> $id_design #> [1] 7 #> strip2$stripsBlockLoc #> $Loc_A #> $Loc_A$rep1 #> D B C A #> E \"E|D\" \"E|B\" \"E|C\" \"E|A\" #> B \"B|D\" \"B|B\" \"B|C\" \"B|A\" #> C \"C|D\" \"C|B\" \"C|C\" \"C|A\" #> D \"D|D\" \"D|B\" \"D|C\" \"D|A\" #> A \"A|D\" \"A|B\" \"A|C\" \"A|A\" #> #> $Loc_A$rep2 #> D B C A #> A \"A|D\" \"A|B\" \"A|C\" \"A|A\" #> B \"B|D\" \"B|B\" \"B|C\" \"B|A\" #> E \"E|D\" \"E|B\" \"E|C\" \"E|A\" #> D \"D|D\" \"D|B\" \"D|C\" \"D|A\" #> C \"C|D\" \"C|B\" \"C|C\" \"C|A\" #> #> $Loc_A$rep3 #> A D C B #> A \"A|A\" \"A|D\" \"A|C\" \"A|B\" #> D \"D|A\" \"D|D\" \"D|C\" \"D|B\" #> E \"E|A\" \"E|D\" \"E|C\" \"E|B\" #> B \"B|A\" \"B|D\" \"B|C\" \"B|B\" #> C \"C|A\" \"C|D\" \"C|C\" \"C|B\" #> #> $Loc_A$rep4 #> A B C D #> A \"A|A\" \"A|B\" \"A|C\" \"A|D\" #> C \"C|A\" \"C|B\" \"C|C\" \"C|D\" #> E \"E|A\" \"E|B\" \"E|C\" \"E|D\" #> B \"B|A\" \"B|B\" \"B|C\" \"B|D\" #> D \"D|A\" \"D|B\" \"D|C\" \"D|D\" #> #> $Loc_A$rep5 #> A C D B #> B \"B|A\" \"B|C\" \"B|D\" \"B|B\" #> C \"C|A\" \"C|C\" \"C|D\" \"C|B\" #> E \"E|A\" \"E|C\" \"E|D\" \"E|B\" #> A \"A|A\" \"A|C\" \"A|D\" \"A|B\" #> D \"D|A\" \"D|C\" \"D|D\" \"D|B\" #> #> $Loc_A$rep6 #> B C D A #> D \"D|B\" \"D|C\" \"D|D\" \"D|A\" #> E \"E|B\" \"E|C\" \"E|D\" \"E|A\" #> B \"B|B\" \"B|C\" \"B|D\" \"B|A\" #> C \"C|B\" \"C|C\" \"C|D\" \"C|A\" #> A \"A|B\" \"A|C\" \"A|D\" \"A|A\" #> #> #> $Loc_B #> $Loc_B$rep1 #> B C D A #> B \"B|B\" \"B|C\" \"B|D\" \"B|A\" #> D \"D|B\" \"D|C\" \"D|D\" \"D|A\" #> E \"E|B\" \"E|C\" \"E|D\" \"E|A\" #> A \"A|B\" \"A|C\" \"A|D\" \"A|A\" #> C \"C|B\" \"C|C\" \"C|D\" \"C|A\" #> #> $Loc_B$rep2 #> D C A B #> D \"D|D\" \"D|C\" \"D|A\" \"D|B\" #> A \"A|D\" \"A|C\" \"A|A\" \"A|B\" #> C \"C|D\" \"C|C\" \"C|A\" \"C|B\" #> E \"E|D\" \"E|C\" \"E|A\" \"E|B\" #> B \"B|D\" \"B|C\" \"B|A\" \"B|B\" #> #> $Loc_B$rep3 #> D A C B #> B \"B|D\" \"B|A\" \"B|C\" \"B|B\" #> E \"E|D\" \"E|A\" \"E|C\" \"E|B\" #> C \"C|D\" \"C|A\" \"C|C\" \"C|B\" #> D \"D|D\" \"D|A\" \"D|C\" \"D|B\" #> A \"A|D\" \"A|A\" \"A|C\" \"A|B\" #> #> $Loc_B$rep4 #> C D A B #> D \"D|C\" \"D|D\" \"D|A\" \"D|B\" #> C \"C|C\" \"C|D\" \"C|A\" \"C|B\" #> E \"E|C\" \"E|D\" \"E|A\" \"E|B\" #> A \"A|C\" \"A|D\" \"A|A\" \"A|B\" #> B \"B|C\" \"B|D\" \"B|A\" \"B|B\" #> #> $Loc_B$rep5 #> A B D C #> D \"D|A\" \"D|B\" \"D|D\" \"D|C\" #> C \"C|A\" \"C|B\" \"C|D\" \"C|C\" #> A \"A|A\" \"A|B\" \"A|D\" \"A|C\" #> B \"B|A\" \"B|B\" \"B|D\" \"B|C\" #> E \"E|A\" \"E|B\" \"E|D\" \"E|C\" #> #> $Loc_B$rep6 #> C D B A #> B \"B|C\" \"B|D\" \"B|B\" \"B|A\" #> D \"D|C\" \"D|D\" \"D|B\" \"D|A\" #> A \"A|C\" \"A|D\" \"A|B\" \"A|A\" #> C \"C|C\" \"C|D\" \"C|B\" \"C|A\" #> E \"E|C\" \"E|D\" \"E|B\" \"E|A\" #> #> #> $Loc_C #> $Loc_C$rep1 #> D A C B #> D \"D|D\" \"D|A\" \"D|C\" \"D|B\" #> B \"B|D\" \"B|A\" \"B|C\" \"B|B\" #> E \"E|D\" \"E|A\" \"E|C\" \"E|B\" #> A \"A|D\" \"A|A\" \"A|C\" \"A|B\" #> C \"C|D\" \"C|A\" \"C|C\" \"C|B\" #> #> $Loc_C$rep2 #> B C A D #> B \"B|B\" \"B|C\" \"B|A\" \"B|D\" #> A \"A|B\" \"A|C\" \"A|A\" \"A|D\" #> D \"D|B\" \"D|C\" \"D|A\" \"D|D\" #> C \"C|B\" \"C|C\" \"C|A\" \"C|D\" #> E \"E|B\" \"E|C\" \"E|A\" \"E|D\" #> #> $Loc_C$rep3 #> C D A B #> E \"E|C\" \"E|D\" \"E|A\" \"E|B\" #> C \"C|C\" \"C|D\" \"C|A\" \"C|B\" #> D \"D|C\" \"D|D\" \"D|A\" \"D|B\" #> A \"A|C\" \"A|D\" \"A|A\" \"A|B\" #> B \"B|C\" \"B|D\" \"B|A\" \"B|B\" #> #> $Loc_C$rep4 #> C D B A #> D \"D|C\" \"D|D\" \"D|B\" \"D|A\" #> A \"A|C\" \"A|D\" \"A|B\" \"A|A\" #> B \"B|C\" \"B|D\" \"B|B\" \"B|A\" #> E \"E|C\" \"E|D\" \"E|B\" \"E|A\" #> C \"C|C\" \"C|D\" \"C|B\" \"C|A\" #> #> $Loc_C$rep5 #> A B D C #> B \"B|A\" \"B|B\" \"B|D\" \"B|C\" #> D \"D|A\" \"D|B\" \"D|D\" \"D|C\" #> A \"A|A\" \"A|B\" \"A|D\" \"A|C\" #> E \"E|A\" \"E|B\" \"E|D\" \"E|C\" #> C \"C|A\" \"C|B\" \"C|D\" \"C|C\" #> #> $Loc_C$rep6 #> C D A B #> B \"B|C\" \"B|D\" \"B|A\" \"B|B\" #> E \"E|C\" \"E|D\" \"E|A\" \"E|B\" #> A \"A|C\" \"A|D\" \"A|A\" \"A|B\" #> D \"D|C\" \"D|D\" \"D|A\" \"D|B\" #> C \"C|C\" \"C|D\" \"C|A\" \"C|B\" #> #> strip2$plotLayouts #> $Loc_A #> $Loc_A$rep1 #> [,1] [,2] [,3] [,4] #> [1,] 101 102 103 104 #> [2,] 105 106 107 108 #> [3,] 109 110 111 112 #> [4,] 113 114 115 116 #> [5,] 117 118 119 120 #> #> $Loc_A$rep2 #> [,1] [,2] [,3] [,4] #> [1,] 201 202 203 204 #> [2,] 205 206 207 208 #> [3,] 209 210 211 212 #> [4,] 213 214 215 216 #> [5,] 217 218 219 220 #> #> $Loc_A$rep3 #> [,1] [,2] [,3] [,4] #> [1,] 301 302 303 304 #> [2,] 305 306 307 308 #> [3,] 309 310 311 312 #> [4,] 313 314 315 316 #> [5,] 317 318 319 320 #> #> $Loc_A$rep4 #> [,1] [,2] [,3] [,4] #> [1,] 401 402 403 404 #> [2,] 405 406 407 408 #> [3,] 409 410 411 412 #> [4,] 413 414 415 416 #> [5,] 417 418 419 420 #> #> $Loc_A$rep5 #> [,1] [,2] [,3] [,4] #> [1,] 501 502 503 504 #> [2,] 505 506 507 508 #> [3,] 509 510 511 512 #> [4,] 513 514 515 516 #> [5,] 517 518 519 520 #> #> $Loc_A$rep6 #> [,1] [,2] [,3] [,4] #> [1,] 601 602 603 604 #> [2,] 605 606 607 608 #> [3,] 609 610 611 612 #> [4,] 613 614 615 616 #> [5,] 617 618 619 620 #> #> #> $Loc_B #> $Loc_B$rep1 #> [,1] [,2] [,3] [,4] #> [1,] 1001 1002 1003 1004 #> [2,] 1005 1006 1007 1008 #> [3,] 1009 1010 1011 1012 #> [4,] 1013 1014 1015 1016 #> [5,] 1017 1018 1019 1020 #> #> $Loc_B$rep2 #> [,1] [,2] [,3] [,4] #> [1,] 1101 1102 1103 1104 #> [2,] 1105 1106 1107 1108 #> [3,] 1109 1110 1111 1112 #> [4,] 1113 1114 1115 1116 #> [5,] 1117 1118 1119 1120 #> #> $Loc_B$rep3 #> [,1] [,2] [,3] [,4] #> [1,] 1201 1202 1203 1204 #> [2,] 1205 1206 1207 1208 #> [3,] 1209 1210 1211 1212 #> [4,] 1213 1214 1215 1216 #> [5,] 1217 1218 1219 1220 #> #> $Loc_B$rep4 #> [,1] [,2] [,3] [,4] #> [1,] 1301 1302 1303 1304 #> [2,] 1305 1306 1307 1308 #> [3,] 1309 1310 1311 1312 #> [4,] 1313 1314 1315 1316 #> [5,] 1317 1318 1319 1320 #> #> $Loc_B$rep5 #> [,1] [,2] [,3] [,4] #> [1,] 1401 1402 1403 1404 #> [2,] 1405 1406 1407 1408 #> [3,] 1409 1410 1411 1412 #> [4,] 1413 1414 1415 1416 #> [5,] 1417 1418 1419 1420 #> #> $Loc_B$rep6 #> [,1] [,2] [,3] [,4] #> [1,] 1501 1502 1503 1504 #> [2,] 1505 1506 1507 1508 #> [3,] 1509 1510 1511 1512 #> [4,] 1513 1514 1515 1516 #> [5,] 1517 1518 1519 1520 #> #> #> $Loc_C #> $Loc_C$rep1 #> [,1] [,2] [,3] [,4] #> [1,] 2001 2002 2003 2004 #> [2,] 2005 2006 2007 2008 #> [3,] 2009 2010 2011 2012 #> [4,] 2013 2014 2015 2016 #> [5,] 2017 2018 2019 2020 #> #> $Loc_C$rep2 #> [,1] [,2] [,3] [,4] #> [1,] 2101 2102 2103 2104 #> [2,] 2105 2106 2107 2108 #> [3,] 2109 2110 2111 2112 #> [4,] 2113 2114 2115 2116 #> [5,] 2117 2118 2119 2120 #> #> $Loc_C$rep3 #> [,1] [,2] [,3] [,4] #> [1,] 2201 2202 2203 2204 #> [2,] 2205 2206 2207 2208 #> [3,] 2209 2210 2211 2212 #> [4,] 2213 2214 2215 2216 #> [5,] 2217 2218 2219 2220 #> #> $Loc_C$rep4 #> [,1] [,2] [,3] [,4] #> [1,] 2301 2302 2303 2304 #> [2,] 2305 2306 2307 2308 #> [3,] 2309 2310 2311 2312 #> [4,] 2313 2314 2315 2316 #> [5,] 2317 2318 2319 2320 #> #> $Loc_C$rep5 #> [,1] [,2] [,3] [,4] #> [1,] 2401 2402 2403 2404 #> [2,] 2405 2406 2407 2408 #> [3,] 2409 2410 2411 2412 #> [4,] 2413 2414 2415 2416 #> [5,] 2417 2418 2419 2420 #> #> $Loc_C$rep6 #> [,1] [,2] [,3] [,4] #> [1,] 2501 2502 2503 2504 #> [2,] 2505 2506 2507 2508 #> [3,] 2509 2510 2511 2512 #> [4,] 2513 2514 2515 2516 #> [5,] 2517 2518 2519 2520 #> #> head(strip2$fieldBook,12) #> ID LOCATION PLOT REP HSTRIP VSTRIP TRT_COMB #> 1 1 A 101 1 E D E|D #> 2 2 A 102 1 E B E|B #> 3 3 A 103 1 E C E|C #> 4 4 A 104 1 E A E|A #> 5 5 A 105 1 B D B|D #> 6 6 A 106 1 B B B|B #> 7 7 A 107 1 B C B|C #> 8 8 A 108 1 B A B|A #> 9 9 A 109 1 C D C|D #> 10 10 A 110 1 C B C|B #> 11 11 A 111 1 C C C|C #> 12 12 A 112 1 C A C|A"},{"path":"https://didiermurillof.github.io/FielDHub/reference/summary.FielDHub.html","id":null,"dir":"Reference","previous_headings":"","what":"Summary a FielDHub object β€” summary.FielDHub","title":"Summary a FielDHub object β€” summary.FielDHub","text":"Summarise information design parameters, data frame structure","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/summary.FielDHub.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summary a FielDHub object β€” summary.FielDHub","text":"","code":"# S3 method for class 'FielDHub' summary(object, ...)"},{"path":"https://didiermurillof.github.io/FielDHub/reference/summary.FielDHub.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summary a FielDHub object β€” summary.FielDHub","text":"object object inheriting class FielDHub ... Unused, extensibility","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/summary.FielDHub.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summary a FielDHub object β€” summary.FielDHub","text":"object inheriting class summary.FielDHub","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/summary.FielDHub.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Summary a FielDHub object β€” summary.FielDHub","text":"Thiago de Paula Oliveira, thiago.paula.oliveira@alumni.usp.br","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/summary.FielDHub.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summary a FielDHub object β€” summary.FielDHub","text":"","code":"# Example 1: Generates a CRD design with 5 treatments and 5 reps each. crd1 <- CRD(t = 5, reps = 5, plotNumber = 101, seed = 1985, locationName = \"Fargo\") crd1$infoDesign #> $numberofTreatments #> [1] 5 #> #> $treatments #> [1] \"T1\" \"T2\" \"T3\" \"T4\" \"T5\" #> #> $Reps #> [1] 5 #> #> $locationName #> [1] \"Fargo\" #> #> $seed #> [1] 1985 #> #> $id_design #> [1] 1 #> summary(crd1) #> Completely Randomized Design (CRD): #> #> 1. Information on the design parameters: #> List of 6 #> $ numberofTreatments: num 5 #> $ treatments : chr [1:5] \"T1\" \"T2\" \"T3\" \"T4\" ... #> $ Reps : num 5 #> $ locationName : chr \"Fargo\" #> $ seed : num 1985 #> $ id_design : num 1 #> #> 2. Structure of the data frame with the CRD field book: #> #> 'data.frame':\t25 obs. of 5 variables: #> $ ID : int 1 2 3 4 5 6 7 8 9 10 ... #> $ LOCATION : chr \"Fargo\" \"Fargo\" \"Fargo\" \"Fargo\" ... #> $ PLOT : int 101 102 103 104 105 106 107 108 109 110 ... #> $ REP : Factor w/ 5 levels \"1\",\"2\",\"3\",\"4\",..: 3 4 2 3 2 2 4 5 1 5 ... #> $ TREATMENT: chr \"T3\" \"T2\" \"T1\" \"T5\" ..."},{"path":"https://didiermurillof.github.io/FielDHub/reference/swap_pairs.html","id":null,"dir":"Reference","previous_headings":"","what":"Swap pairs in a matrix of integers β€” swap_pairs","title":"Swap pairs in a matrix of integers β€” swap_pairs","text":"Modifies input matrix X ensure distance two occurrences integer least dist d, swapping one occurrences random occurrence different integer least d away. function starts starting_dist = 3 increases 1 algorithm longer converges stop_iter iterations performed.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/swap_pairs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Swap pairs in a matrix of integers β€” swap_pairs","text":"","code":"swap_pairs(X, starting_dist = 3, stop_iter = 50)"},{"path":"https://didiermurillof.github.io/FielDHub/reference/swap_pairs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Swap pairs in a matrix of integers β€” swap_pairs","text":"X matrix integers. starting_dist minimum starting distance enforce pairs occurrences integer. Default 3. stop_iter maximum number iterations perform. Default 100.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/swap_pairs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Swap pairs in a matrix of integers β€” swap_pairs","text":"list containing following elements: optim_design modified matrix. designs list intermediate designs, starting input matrix. distances list pair distances intermediate design. min_distance integer indicating minimum distance pairs occurrences integer. pairwise_distance data frame pairwise distances final design.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/swap_pairs.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Swap pairs in a matrix of integers β€” swap_pairs","text":"Jean-Marc Montpetit [aut], Didier Murillo [aut]","code":""},{"path":"https://didiermurillof.github.io/FielDHub/reference/swap_pairs.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Swap pairs in a matrix of integers β€” swap_pairs","text":"","code":"# Create a matrix X with the numbers 1 to 10 are twice and 11 to 50 are once. # The matrix has 6 rows and 10 columns set.seed(123) X <- matrix(sample(c(rep(1:10, 2), 11:50), replace = FALSE), ncol = 10) X #> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] #> [1,] 21 40 17 25 26 3 11 31 36 6 #> [2,] 5 33 5 8 48 29 43 23 1 45 #> [3,] 41 27 38 39 7 28 14 22 24 4 #> [4,] 4 47 18 7 2 35 6 20 12 46 #> [5,] 3 15 9 34 49 50 2 10 42 8 #> [6,] 32 16 19 9 10 13 37 1 44 30 # Swap pairs B <- swap_pairs(X, starting_dist = 3) B$optim_design #> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] #> [1,] 4 40 17 8 47 5 11 18 10 7 #> [2,] 7 30 22 3 48 29 34 44 25 1 #> [3,] 32 33 6 12 13 28 37 6 21 9 #> [4,] 9 1 20 2 19 35 43 38 15 16 #> [5,] 46 5 26 27 49 23 14 45 39 31 #> [6,] 36 8 42 41 50 10 24 3 4 2 B$designs #> [[1]] #> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] #> [1,] 21 40 17 25 26 3 11 31 36 6 #> [2,] 5 33 5 8 48 29 43 23 1 45 #> [3,] 41 27 38 39 7 28 14 22 24 4 #> [4,] 4 47 18 7 2 35 6 20 12 46 #> [5,] 3 15 9 34 49 50 2 10 42 8 #> [6,] 32 16 19 9 10 13 37 1 44 30 #> #> [[2]] #> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] #> [1,] 21 40 17 25 26 3 11 9 23 6 #> [2,] 7 33 15 36 48 29 43 8 1 45 #> [3,] 41 8 38 39 13 28 14 22 24 2 #> [4,] 4 47 18 2 9 35 6 20 12 46 #> [5,] 3 5 31 4 49 50 34 10 42 7 #> [6,] 32 16 19 27 10 5 37 1 44 30 #> #> [[3]] #> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] #> [1,] 21 40 17 25 26 3 11 9 23 6 #> [2,] 7 33 15 36 48 29 43 8 4 45 #> [3,] 41 8 38 39 13 28 14 22 24 2 #> [4,] 46 47 18 2 9 35 6 20 12 10 #> [5,] 10 5 31 1 49 50 34 3 42 7 #> [6,] 32 16 19 27 4 5 37 1 44 30 #> #> [[4]] #> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] #> [1,] 4 40 17 8 47 5 11 18 10 7 #> [2,] 7 30 22 3 48 29 34 44 25 1 #> [3,] 32 33 6 12 13 28 37 6 21 9 #> [4,] 9 1 20 2 19 35 43 38 15 16 #> [5,] 46 5 26 27 49 23 14 45 39 31 #> [6,] 36 8 42 41 50 10 24 3 4 2 #> B$distances #> [[1]] #> geno Pos1 Pos2 DIST rA cA rB cB #> 7 7 22 27 1.414214 4 4 3 5 #> 9 9 17 24 1.414214 5 3 6 4 #> 5 5 2 14 2.000000 2 1 2 3 #> 2 2 28 41 2.236068 4 5 5 7 #> 10 10 30 47 3.162278 6 5 5 8 #> 1 1 48 50 4.123106 6 8 2 9 #> 6 6 40 55 4.242641 4 7 1 10 #> 3 3 5 31 6.403124 5 1 1 6 #> 8 8 20 59 6.708204 2 4 5 10 #> 4 4 4 57 9.055385 4 1 3 10 #> #> [[2]] #> geno Pos1 Pos2 DIST rA cA rB cB #> 4 4 4 23 3.162278 4 1 5 4 #> 10 10 30 47 3.162278 6 5 5 8 #> 1 1 48 50 4.123106 6 8 2 9 #> 5 5 11 36 4.123106 5 2 6 6 #> 6 6 40 55 4.242641 4 7 1 10 #> 9 9 28 43 4.242641 4 5 1 8 #> 2 2 22 57 6.082763 4 4 3 10 #> 8 8 9 44 6.082763 3 2 2 8 #> 3 3 5 31 6.403124 5 1 1 6 #> 7 7 2 59 9.486833 2 1 5 10 #> #> [[3]] #> geno Pos1 Pos2 DIST rA cA rB cB #> 1 1 23 48 4.123106 5 4 6 8 #> 5 5 11 36 4.123106 5 2 6 6 #> 6 6 40 55 4.242641 4 7 1 10 #> 9 9 28 43 4.242641 4 5 1 8 #> 3 3 31 47 4.472136 1 6 5 8 #> 4 4 30 50 5.656854 6 5 2 9 #> 2 2 22 57 6.082763 4 4 3 10 #> 8 8 9 44 6.082763 3 2 2 8 #> 10 10 5 58 9.055385 5 1 4 10 #> 7 7 2 59 9.486833 2 1 5 10 #> #> [[4]] #> geno Pos1 Pos2 DIST rA cA rB cB #> 6 6 15 45 5.000000 3 3 3 8 #> 8 8 12 19 5.385165 6 2 1 4 #> 3 3 20 48 5.656854 2 4 6 8 #> 5 5 11 31 5.656854 5 2 1 6 #> 10 10 36 49 5.830952 6 6 1 9 #> 2 2 22 60 6.324555 4 4 6 10 #> 1 1 10 56 8.246211 4 2 2 10 #> 7 7 2 55 9.055385 2 1 1 10 #> 9 9 4 57 9.055385 4 1 3 10 #> 4 4 1 54 9.433981 1 1 6 9 #>"},{"path":"https://didiermurillof.github.io/FielDHub/news/index.html","id":"fieldhub-131","dir":"Changelog","previous_headings":"","what":"FielDHub 1.3.1","title":"FielDHub 1.3.1","text":"CRAN release: 2023-04-20","code":""},{"path":"https://didiermurillof.github.io/FielDHub/news/index.html","id":"new-features-in-the-shiny-app-1-3-1","dir":"Changelog","previous_headings":"","what":"New Features in the Shiny App","title":"FielDHub 1.3.1","text":"Added module generate Sparse allocation. Added module generating Optimized Multi-Location Partially Replicated (p-rep). Added vignettes help documentation new modules; Sparse Allocations Optimized Multi-Location Partially Replicated (p-rep) Designs app.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/news/index.html","id":"enhancements-1-3-1","dir":"Changelog","previous_headings":"","what":"Enhancements:","title":"FielDHub 1.3.1","text":"Renamed Partially Replicated module Single Multi-Location p-rep Improved usability field dimensions dropdown menu reordering options based absolute value difference number rows columns option. affects unreplicated partially replicated design modules.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/news/index.html","id":"fix-bugs-1-3-1","dir":"Changelog","previous_headings":"","what":"Fix bugs:","title":"FielDHub 1.3.1","text":"Fixed issue: Upload data CRD module.","code":""},{"path":[]},{"path":"https://didiermurillof.github.io/FielDHub/news/index.html","id":"standalone-functions-1-3-1","dir":"Changelog","previous_headings":"","what":"Standalone Functions","title":"FielDHub 1.3.1","text":"Created do_optim() function. function generates sparse p-rep allocation multiple locations. optimized allocation using incomplete blocks. Created sparse_allocation() function. new function uses function, do_optim(), generate sparse allocation, uses function diagonal_arrangement() create unreplicated designs across multiple locations. Created multi_location_prep() function. uses within optimization function do_optim() generate partially replicated (p-rep) allocation, uses function partially_replicated() create p-rep designs across multiple locations. Created pairs_distance() function. function calculates pairwise distances elements matrix appears twice . Created swap_pairs() function. swaps pairs matrix integers optimizes p-rep design. function modifies input matrix XX ensure distance two occurrences integer least distance dd, swapping one occurrences random occurrence different integer least dd away. function starts starting dist d=3d = 3 increases 11 algorithm longer converges max number iterations performed. Created search_matrix_values() function. looks values appear row matrix return row number, value, frequency. Added optimization process partially replicated (p-rep) designs. uses function swap_pairs(). Added vignettes help documentation new functions.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/news/index.html","id":"enhancements-1-3-1-1","dir":"Changelog","previous_headings":"","what":"Enhancements:","title":"FielDHub 1.3.1","text":"partially_replicated() accepts custom field dimensions location. example, nrows = c(23, 20, 20) ncols = c(20, 23, 23) field rows columns three environments. Code refactoring diagonal_arrangement() function. Code refactoring utility function pREP(). Avoid cyclic reps incomplete block designs number treatments square.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/news/index.html","id":"fieldhub-120","dir":"Changelog","previous_headings":"","what":"FielDHub 1.2.0","title":"FielDHub 1.2.0","text":"CRAN release: 2022-08-05","code":""},{"path":"https://didiermurillof.github.io/FielDHub/news/index.html","id":"shiny-app-1-2-0","dir":"Changelog","previous_headings":"","what":"Shiny App","title":"FielDHub 1.2.0","text":"Added help menu option app connect directly documentation available GitHub repository. Added vignettes help documentation standard functions modules available designs app. Added capability making multiple randomizations across different locations unreplicated, partially replicated, lattice, RCBD, factorial, split-plot, split-split-plot, strip-plot, IBD, RCD designs. Added capability produce heatmap visualizations simulated data experimental designs. Added action buttons copy save field maps field book outputs Excel. Added factorization options aid users creation randomizations mapping layouts unreplicated partially replicated designs. Previous version required users * mathematical calculation priori. Added filters search boxes field book tables. Updated UI/UX design home page. Grouped single diagonal arrangement, multiple diagonal arrangement, optimized arrangement augmented RCB designs one single module. Added action run button experimental designs prevent reactivity issues application. Improved standardized user experience features readability access. Improved error logging messages. Added features inform end-users utilization correct input data file formats associated metadata/columns, checking duplicate values input files, well data type verification. Added plot() method FielDHub package display field layout field book designs. Added additional field layout visualization/map options experimental designs. Previous version mapping options unreplicated p-rep designs. Added drop-menu display multiple layout mapping option shown entry number plot experimental designs. means, now can visualize randomization layout option locations input. Added option repeating whole entries/experiments unreplicated diagonal arrangement design multiple experiments (previously called decision blocks). Added check box feature Augmented RCB design allow creation nurseries option randomizing experimental entries . user decides leave option unchecked, checks randomized, experimental entries shown consecutive order. Added check box option RCB design allow continuous plot numbering independently rep block number. Previous version coded replication plot number (.e., 101 =rep1, 201=rep2, etc.). Fixed restriction RCBD mapping layout allow use 25 entries. PS: better designs number entries higher 25 (info go : FIELD PLOT DESIGN ).","code":""},{"path":"https://didiermurillof.github.io/FielDHub/news/index.html","id":"standalone-functions-in-fieldhub-package-1-2-0","dir":"Changelog","previous_headings":"","what":"Standalone Functions in FielDHub Package","title":"FielDHub 1.2.0","text":"partially_replicated() now generates randomization across multiple locations/sites. diagonal_arrangement() now generates randomization across multiple locations/sites. optimized_arrangement() now generates randomization across multiple locations/sites. partially_replicated() now allows entries/treatments replicates. , required least unreplicated entries. Functions optimized_arrangement(), diagonal_arrangement() partially_replicated() now return feedback input dimensions nrows ncols incorrect. RCBD() now includes argument (continuous) manage way sets plotting number. RCBD_augmented() now allows customization field dimensions inputting number rows columns nrows ncols arguments. RCBD_augmented() now returns feedback input dimensions nrows ncols match data entered. RCBD_augmented() random = FALSE now allows randomizing checks/controls user wants. Fixed bug full_factorial() CRD factorial design prevented option including possible factorial combinations. Added method print() class fieldLayout. See print(). Added method plot() class FieldHub returns object class fieldLayout. See plot(). method plot() can plot field layout designs output. possible pass arguments location, layout order others. detail see plot(), print() summary() methods FielDHub. Fixed bug diagonal_arrangement() kindExpt = DBUDC. problem affected random distribution checks case unbalanced control plot numbers experiment. Fixed bug diagonal_arrangement() kindExpt = DBUDC. problem affected merging data user data randomization data users wanted replicated entries across experiments.","code":""},{"path":"https://didiermurillof.github.io/FielDHub/news/index.html","id":"fieldhub-010","dir":"Changelog","previous_headings":"","what":"FielDHub 0.1.0","title":"FielDHub 0.1.0","text":"CRAN release: 2021-05-19 CRAN release.","code":""}]