Fixed nets and Confidence intervals

.Rhistory

@@ -1,60 +1,3 @@
-Summs.exp$Normal.Resid <- ifelse(tidy(ks.test(augment(exp.model)$.resid,y='pnorm',alternative='two.sided'))$p.value < 0.05, "No", "Yes")
-Summs.exp$family <- "Exponential"
-Summs.exp$AICcNorm <- logLik(MASS::fitdistr(augment(exp.model)$.resid, "normal"))[1]
-Summs.exp$AICcNorm <- (4 - 2*Summs.exp$AICcNorm) + (12/(nrow(augment(exp.model)) - 1))
-Params.exp <- tidy(exp.model)
-Params.exp$model <- "Exp"
-}, error = function(e) {
-# display error message when there is an error
-message("Could not fit Exponential model: ", e$message)
-# set Summs.exp to NULL when there is an error
-Summs.exp <- NULL
-})
-#exponential model Log
-tryCatch({
-power <- filter(For.Graph, K != 0 & Cumulative != 0)
-logexp.model <- glm(LogCum ~ K, data = power)
-power$LogExp <- exp(predict(logexp.model))
-Summs.logexp <- glance(logexp.model)
-Summs.logexp$model <- "LogExp"
-Summs.logexp$Normal.Resid <- ifelse(tidy(ks.test(augment(logexp.model)$.resid,y='pnorm',alternative='two.sided'))$p.value < 0.05, "No", "Yes")
-Summs.logexp$family <- "Exponential"
-Summs.logexp$AICcNorm <- logLik(MASS::fitdistr(augment(logexp.model)$.resid, "normal"))[1]
-Summs.logexp$AICcNorm <- (4 - 2*Summs.logexp$AICcNorm) + (12/(nrow(augment(logexp.model)) - 1))
-Params.logexp <- tidy(logexp.model)
-Params.logexp$model <- "LogExp"}, error = function(e) {
-# display error message when there is an error
-message("Could not fit Logexponential model: ", e$message)
-# set Summs.exp to NULL when there is an error
-Summs.logexp <- NULL
-})
-#logpowerlaw
-tryCatch({
-logpower.model <- glm(LogCum ~ I(log(K)), data = power)
-power$LogPower <- exp(predict(logpower.model))
-For.Graph <- full_join(For.Graph, power)
-Summs.logpower <- glance(logpower.model)
-Summs.logpower$model <- "LogPower"
-Summs.logpower$Normal.Resid <- ifelse(tidy(ks.test(augment(logpower.model)$.resid,y='pnorm',alternative='two.sided'))$p.value < 0.05, "No", "Yes")
-Summs.logpower$family <- "PowerLaw"
-Summs.logpower$AICcNorm <- logLik(MASS::fitdistr(augment(logpower.model)$.resid, "normal"))[1]
-Summs.logpower$AICcNorm <- (4 - 2*Summs.logpower$AICcNorm) + (12/(nrow(augment(logpower.model)) - 1))
-Params.logpower <- tidy(logpower.model)
-Params.logpower$model <- "LogPower"}, error = function(e) {
-# display error message when there is an error
-message("Could not fit Logpower model: ", e$message)
-# set Summs.exp to NULL when there is an error
-Summs.logpower <- NULL
-})
-#powerlaw
-tryCatch({
-powerlaw.model <- nls(Cumulative~a*K^y, start= list(y=0, a = 1), data = power)
-power$Power <- predict(powerlaw.model)
-For.Graph <- full_join(For.Graph, power)
-Summs.power <- glance(powerlaw.model)
-Summs.power$model <- "Power"
-Summs.power$Normal.Resid  <- ifelse(tidy(ks.test(augment(powerlaw.model)$.resid,y='pnorm',alternative='two.sided'))$p.value < 0.05, "No", "Yes")
-Summs.power$family <- "PowerLaw"
 Summs.power$AICcNorm <- logLik(MASS::fitdistr(augment(powerlaw.model)$.resid, "normal"))[1]
 Summs.power$AICcNorm <- (4 - 2*Summs.power$AICcNorm) + (12/(nrow(augment(powerlaw.model)) - 1))
 Params.power <- tidy(powerlaw.model)
@@ -510,3 +453,60 @@ usethis::use_package("doSNOW")
 library(NetworkExtinction)
 library(NetworkExtinction)
 library(NetworkExtinction)
+library(NetworkExtinction)
+?ExtinctionOrder
+data("net")
+SimulateExtinctions(Network = net, Order = c(1,2,3,4,5,6,7,8,9,10),
+Method = "Ordered" , clust.method = "cluster_infomap")
+data("net")
+Test <- SimulateExtinctions(Network = net, Order = c(1,2,3,4,5,6,7,8,9,10),
+Method = "Ordered" , clust.method = "cluster_infomap")
+Test$sims
+Test$R50
+Test$R100
+library(NetworkExtinction)
+library(NetworkExtinction)
+data("net")
+Test <- SimulateExtinctions(Network = net, Order = c(1,2,3,4,5,6,7,8,9,10),
+Method = "Ordered" , clust.method = "cluster_infomap")
+Test
+Test$R50
+Test$R100
+library(NetworkExtinction)
+data("More_Connected")
+Test <- RandomExtinctions(Network = More_Connected, nsim = 20)
+Test$R50
+data("More_Connected")
+Test <- RandomExtinctions(Network = More_Connected, nsim = 20)
+library(NetworkExtinction)
+data("More_Connected")
+Test <- RandomExtinctions(Network = More_Connected, nsim = 20)
+Test$R100
+data("More_Connected")
+Test <- RandomExtinctions(Network = More_Connected, nsim = 20, Record = F, plot = F)
+library(NetworkExtinction)
+library(NetworkExtinction)
+data("More_Connected")
+Test <- RandomExtinctions(Network = More_Connected, nsim = 20, Record = F, plot = F)
+data("More_Connected")
+Test <- RandomExtinctions(Network = More_Connected, nsim = 20, Record = T, plot = F)
+library(NetworkExtinction)
+Test <- RandomExtinctions(Network = More_Connected, nsim = 20)
+sims <- readRDS("~/Documents/NetworkExtinction/sims.rds")
+class(sims)
+sims
+library(NetworkExtinction)
+Test <- RandomExtinctions(Network = More_Connected, nsim = 20)
+sims <- readRDS("~/Documents/NetworkExtinction/sims.rds")
+sims
+sims[[1]]$R50
+library(NetworkExtinction)
+Test <- RandomExtinctions(Network = More_Connected, nsim = 20)
+Test$sims
+Test$nets
+Test$``
+library(NetworkExtinction)
+Test <- RandomExtinctions(Network = More_Connected, nsim = 20)
+Test$R50result
+Test$R100result
+library(NetworkExtinction)

.Rproj.user/shared/notebooks/paths

@@ -1,2 +1,4 @@
 /home/au687614/Documents/NetworkExtinction/DESCRIPTION="4BD5E975"
 /home/au687614/Documents/NetworkExtinction/README.Rmd="7DA4CF08"
+/home/au687614/Downloads/Robustness metrics V3(1).R="21EBD13A"
+/home/au687614/Downloads/Robustness metrics V3.R="41B517CD"

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: NetworkExtinction
 Type: Package
 Title: Extinction Simulation in Ecological Networks
-Version: 1.0.2
+Version: 1.0.3
 Authors@R: c(person("Derek", "Corcoran", email = "[email protected]",
   role = c("aut", "cre"), comment = c(ORCID = "0000-0002-0248-6230")), person("M. Isidora", "Ávila-Thieme", email = "[email protected]",
   role = c("aut"), comment = c(ORCID = "0000-0003-0772-4717")), person("Fernanda S.", "Valdovinos", role = c("aut")), person("Sergio A.", "Navarrete", role = c("aut")), 

R/Extintions.R

@@ -730,13 +730,15 @@ RandomExtinctions <- function(Network, nsim = 10,
   R50mean <- mean(r50values)
   R50CI <- 1.96*sd(r50values)
   R50result <- c(R50mean,R50CI)
+  names(R50result) <- c("Mean", "CI")
 
   R100mean <- mean(r100values)
   R100CI <- 1.96*sd(r100values)
   R100result <- c(R100mean,R100CI)
+  names(R50result) <- c("Mean", "CI")
 
   ## extract objects
-  temps <- lapply(sims, "[[", 2)
+  temps <- lapply(sims, "[[", 4)
   sims <- lapply(sims, "[[", 1)
   cond <- sapply(sims, function(x) "data.frame" %in% class(x))
   cond <- c(1:length(cond))[cond]

R/Powerlaw.R

@@ -166,15 +166,15 @@ DegreeDistribution <- function(Network, scale = "arithmetic"){
 
 
   #all together
-  Summs <- list(Summs.exp, Summs.power, Summs.logexp, Summs.logpower) |>
-    purrr::discard(is.null) |>
-    purrr::reduce(full_join) |>
-    select(logLik, AIC, BIC, model, Normal.Resid, family, AICcNorm) |>      arrange(Normal.Resid, AIC) |>
-    dplyr::select(logLik, AIC, BIC, model, Normal.Resid, family) |>
+  Summs <- list(Summs.exp, Summs.power, Summs.logexp, Summs.logpower) %>%
+    purrr::discard(is.null) %>%
+    purrr::reduce(full_join) %>%
+    select(logLik, AIC, BIC, model, Normal.Resid, family, AICcNorm) %>%      arrange(Normal.Resid, AIC) %>%
+    dplyr::select(logLik, AIC, BIC, model, Normal.Resid, family) %>%
     dplyr::filter(AIC > -Inf, AIC < Inf)
 
 
-  params <- list(Params.logpower, Params.power, Params.logexp, Params.exp) |> purrr::discard(is.null) |>
+  params <- list(Params.logpower, Params.power, Params.logexp, Params.exp) %>% purrr::discard(is.null) %>%
     purrr::reduce(bind_rows) %>%
     dplyr::filter(model %in% Summs$model) %>%
     mutate(term = case_when(term == "y" ~ "Beta",