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Type issue with weights + gtsummary #23

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LucyMcGowan opened this issue Mar 3, 2025 · 1 comment
Closed

Type issue with weights + gtsummary #23

LucyMcGowan opened this issue Mar 3, 2025 · 1 comment

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@LucyMcGowan
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library(touringplans)
library(propensity)
library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
library(broom)

seven_dwarfs_9 <- seven_dwarfs_train_2018 %>%
  filter(wait_hour == 9)

propensity_model <- glm(park_extra_magic_morning ~ park_close + park_temperature_high +
                          park_ticket_season, data = seven_dwarfs_9, family = binomial())

seven_dwarfs_9 <- propensity_model |>
  augment(type.predict = "response", data = seven_dwarfs_9) |>
  mutate(w_ate = wt_ate(.fitted, park_extra_magic_morning, exposure_type = "binary"),
         w_atm = wt_atm(.fitted, park_extra_magic_morning, exposure_type = "binary"),
         w_ato = wt_ato(.fitted, park_extra_magic_morning, exposure_type = "binary"),
         w_att = wt_att(.fitted, park_extra_magic_morning, exposure_type = "binary"))

seven_dwarfs_9 <- seven_dwarfs_9 %>%
  mutate(park_close = as.numeric(park_close))

library(gtsummary)
library(survey)
#> Loading required package: grid
#> Loading required package: Matrix
#> Loading required package: survival
#> 
#> Attaching package: 'survey'
#> The following object is masked from 'package:graphics':
#> 
#>     dotchart

svy_des <- svydesign(
  ids = ~ 1,
  data = seven_dwarfs_9,
  weights = ~ w_ato
)

tbl_svysummary(svy_des, by = park_extra_magic_morning) %>%
  add_difference(everything() ~ "smd")
#> Warning in svymean.survey.design2(reformulate2(variable), design = data, :
#> Sample size greater than population size: are weights correctly scaled?
#> Warning in svymean.survey.design2(data, design[byfactor %in% byfactor[i], :
#> Sample size greater than population size: are weights correctly scaled?
#> Warning in svymean.survey.design2(data, design[byfactor %in% byfactor[i], :
#> Sample size greater than population size: are weights correctly scaled?
#> Warning in svymean.survey.design2(data, design[byfactor %in% byfactor[i], :
#> Sample size greater than population size: are weights correctly scaled?
#> Warning in svymean.survey.design2(data, design[byfactor %in% byfactor[i], :
#> Sample size greater than population size: are weights correctly scaled?
#> Warning in svymean.survey.design2(data, design[byfactor %in% byfactor[i], :
#> Sample size greater than population size: are weights correctly scaled?
#> Warning in svymean.survey.design2(data, design[byfactor %in% byfactor[i], :
#> Sample size greater than population size: are weights correctly scaled?
#> Warning in svymean.survey.design2(data, design[byfactor %in% byfactor[i], :
#> Sample size greater than population size: are weights correctly scaled?
#> Warning in svymean.survey.design2(data, design[byfactor %in% byfactor[i], :
#> Sample size greater than population size: are weights correctly scaled?
#> Warning in svymean.survey.design2(data, design[byfactor %in% byfactor[i], :
#> Sample size greater than population size: are weights correctly scaled?
#> Warning in svymean.survey.design2(data, design[byfactor %in% byfactor[i], :
#> Sample size greater than population size: are weights correctly scaled?
#> Warning in svymean.survey.design2(data, design[byfactor %in% byfactor[i], :
#> Sample size greater than population size: are weights correctly scaled?
#> Warning in svymean.survey.design2(data, design[byfactor %in% byfactor[i], :
#> Sample size greater than population size: are weights correctly scaled?
#> Warning in svymean.survey.design2(data, design[byfactor %in% byfactor[i], :
#> Sample size greater than population size: are weights correctly scaled?
#> Warning in svymean.survey.design2(data, design[byfactor %in% byfactor[i], :
#> Sample size greater than population size: are weights correctly scaled?
#> Warning in svymean.survey.design2(data, design[byfactor %in% byfactor[i], :
#> Sample size greater than population size: are weights correctly scaled?
#> Warning in svymean.survey.design2(data, design[byfactor %in% byfactor[i], :
#> Sample size greater than population size: are weights correctly scaled?
#> Warning in svymean.survey.design2(data, design[byfactor %in% byfactor[i], :
#> Sample size greater than population size: are weights correctly scaled?
#> Warning in svymean.survey.design2(data, design[byfactor %in% byfactor[i], :
#> Sample size greater than population size: are weights correctly scaled?
#> Warning in svymean.survey.design2(data, design[byfactor %in% byfactor[i], :
#> Sample size greater than population size: are weights correctly scaled?
#> Warning in svymean.survey.design2(data, design[byfactor %in% byfactor[i], :
#> Sample size greater than population size: are weights correctly scaled?
#> Warning in svymean.survey.design2(data, design[byfactor %in% byfactor[i], :
#> Sample size greater than population size: are weights correctly scaled?
#> Warning in svymean.survey.design2(data, design[byfactor %in% byfactor[i], :
#> Sample size greater than population size: are weights correctly scaled?
#> Warning in svymean.survey.design2(data, design[byfactor %in% byfactor[i], :
#> Sample size greater than population size: are weights correctly scaled?
#> Warning in svymean.survey.design2(data, design[byfactor %in% byfactor[i], :
#> Sample size greater than population size: are weights correctly scaled?
#> Warning in svymean.survey.design2(data, design[byfactor %in% byfactor[i], :
#> Sample size greater than population size: are weights correctly scaled?
#> Warning in svymean.survey.design2(data, design[byfactor %in% byfactor[i], :
#> Sample size greater than population size: are weights correctly scaled?
#> Warning in svymean.survey.design2(data, design[byfactor %in% byfactor[i], :
#> Sample size greater than population size: are weights correctly scaled?
#> Warning in svymean.survey.design2(data, design[byfactor %in% byfactor[i], :
#> Sample size greater than population size: are weights correctly scaled?
#> Warning in svymean.survey.design2(data, design[byfactor %in% byfactor[i], :
#> Sample size greater than population size: are weights correctly scaled?
#> Warning in svymean.survey.design2(data, design[byfactor %in% byfactor[i], :
#> Sample size greater than population size: are weights correctly scaled?
#> Warning in svymean.survey.design2(data, design[byfactor %in% byfactor[i], :
#> Sample size greater than population size: are weights correctly scaled?
#> Warning in svymean.survey.design2(data, design[byfactor %in% byfactor[i], :
#> Sample size greater than population size: are weights correctly scaled?
#> Warning in svymean.survey.design2(data, design[byfactor %in% byfactor[i], :
#> Sample size greater than population size: are weights correctly scaled?
#> Warning in svymean.survey.design2(data, design[byfactor %in% byfactor[i], :
#> Sample size greater than population size: are weights correctly scaled?
#> Warning in svymean.survey.design2(data, design[byfactor %in% byfactor[i], :
#> Sample size greater than population size: are weights correctly scaled?
#> Warning in svymean.survey.design2(data, design[byfactor %in% byfactor[i], :
#> Sample size greater than population size: are weights correctly scaled?
#> Warning in svymean.survey.design2(data, design[byfactor %in% byfactor[i], :
#> Sample size greater than population size: are weights correctly scaled?
#> Warning in svymean.survey.design2(data, design[byfactor %in% byfactor[i], :
#> Sample size greater than population size: are weights correctly scaled?
#> Warning in svymean.survey.design2(data, design[byfactor %in% byfactor[i], :
#> Sample size greater than population size: are weights correctly scaled?
#> Warning in svymean.survey.design2(data, design[byfactor %in% byfactor[i], :
#> Sample size greater than population size: are weights correctly scaled?
#> Warning in svymean.survey.design2(data, design[byfactor %in% byfactor[i], :
#> Sample size greater than population size: are weights correctly scaled?
#> Warning in svymean.survey.design2(data, design[byfactor %in% byfactor[i], :
#> Sample size greater than population size: are weights correctly scaled?
#> Warning in svymean.survey.design2(data, design[byfactor %in% byfactor[i], :
#> Sample size greater than population size: are weights correctly scaled?
#> Warning in svymean.survey.design2(data, design[byfactor %in% byfactor[i], :
#> Sample size greater than population size: are weights correctly scaled?
#> Warning in svymean.survey.design2(data, design[byfactor %in% byfactor[i], :
#> Sample size greater than population size: are weights correctly scaled?
#> Warning in svymean.survey.design2(data, design[byfactor %in% byfactor[i], :
#> Sample size greater than population size: are weights correctly scaled?
#> Warning in svymean.survey.design2(reformulate2(variable), design = data, :
#> Sample size greater than population size: are weights correctly scaled?
#> Error in `as.character()`:
#> ! Can't convert `x` <psw{estimand = ate}> to <character>.

Created on 2025-03-03 with reprex v2.1.1

Session info 
sessioninfo::session_info()
#> Warning in system2("quarto", "-V", stdout = TRUE, env = paste0("TMPDIR=", :
#> running command '"quarto"
#> TMPDIR=C:/Users/Lauren/AppData/Local/Temp/RtmpgxfI0A/filea89c341e68be -V' had
#> status 1
#> ─ Session info ───────────────────────────────────────────────────────────────
#>  setting  value
#>  version  R version 4.4.2 (2024-10-31 ucrt)
#>  os       Windows 10 x64 (build 19045)
#>  system   x86_64, mingw32
#>  ui       RTerm
#>  language (EN)
#>  collate  English_United States.utf8
#>  ctype    English_United States.utf8
#>  tz       America/New_York
#>  date     2025-03-03
#>  pandoc   3.2 @ C:/Program Files/RStudio/resources/app/bin/quarto/bin/tools/ (via rmarkdown)
#>  quarto   NA @ C:\\PROGRA~1\\RStudio\\RESOUR~1\\app\\bin\\quarto\\bin\\quarto.exe
#> 
#> ─ Packages ───────────────────────────────────────────────────────────────────
#>  package      * version    date (UTC) lib source
#>  backports      1.5.0      2024-05-23 [1] CRAN (R 4.4.0)
#>  broom        * 1.0.7      2024-09-26 [1] CRAN (R 4.4.2)
#>  cards          0.5.0      2025-02-17 [1] CRAN (R 4.4.2)
#>  cardx          0.2.3      2025-02-18 [1] CRAN (R 4.4.2)
#>  cli            3.6.3      2024-06-21 [1] CRAN (R 4.4.1)
#>  DBI            1.2.3      2024-06-02 [1] CRAN (R 4.4.1)
#>  digest         0.6.37     2024-08-19 [1] CRAN (R 4.4.1)
#>  dplyr        * 1.1.4      2023-11-17 [1] CRAN (R 4.4.1)
#>  evaluate       1.0.3      2025-01-10 [1] CRAN (R 4.4.2)
#>  fastmap        1.2.0      2024-05-15 [1] CRAN (R 4.4.1)
#>  fs             1.6.5      2024-10-30 [1] CRAN (R 4.4.2)
#>  generics       0.1.3      2022-07-05 [1] CRAN (R 4.4.1)
#>  glue           1.8.0      2024-09-30 [1] CRAN (R 4.4.2)
#>  gtsummary    * 2.0.4      2024-11-30 [1] CRAN (R 4.4.2)
#>  htmltools      0.5.8.1    2024-04-04 [1] CRAN (R 4.4.1)
#>  knitr          1.49       2024-11-08 [1] CRAN (R 4.4.2)
#>  lattice        0.22-6     2024-03-20 [2] CRAN (R 4.4.2)
#>  lifecycle      1.0.4      2023-11-07 [1] CRAN (R 4.4.1)
#>  magrittr       2.0.3      2022-03-30 [1] CRAN (R 4.4.1)
#>  Matrix       * 1.7-1      2024-10-18 [2] CRAN (R 4.4.2)
#>  mitools        2.4        2019-04-26 [1] CRAN (R 4.4.2)
#>  pillar         1.10.1     2025-01-07 [1] CRAN (R 4.4.2)
#>  pkgconfig      2.0.3      2019-09-22 [1] CRAN (R 4.4.1)
#>  propensity   * 0.0.0.9000 2025-02-26 [1] Github (r-causal/propensity@d569c89)
#>  purrr          1.0.2      2023-08-10 [1] CRAN (R 4.4.1)
#>  R6             2.5.1      2021-08-19 [1] CRAN (R 4.4.1)
#>  Rcpp           1.0.13     2024-07-17 [1] CRAN (R 4.4.1)
#>  reprex         2.1.1      2024-07-06 [1] CRAN (R 4.4.1)
#>  rlang          1.1.4      2024-06-04 [1] CRAN (R 4.4.1)
#>  rmarkdown      2.29       2024-11-04 [1] CRAN (R 4.4.2)
#>  rstudioapi     0.17.1     2024-10-22 [1] CRAN (R 4.4.2)
#>  sessioninfo    1.2.3      2025-02-05 [1] CRAN (R 4.4.2)
#>  survey       * 4.4-2      2024-03-20 [1] CRAN (R 4.4.2)
#>  survival     * 3.8-3      2024-12-17 [1] CRAN (R 4.4.2)
#>  tibble         3.2.1      2023-03-20 [1] CRAN (R 4.4.1)
#>  tidyr          1.3.1      2024-01-24 [1] CRAN (R 4.4.1)
#>  tidyselect     1.2.1      2024-03-11 [1] CRAN (R 4.4.1)
#>  touringplans * 0.0.1      2025-02-24 [1] Github (LucyMcGowan/touringplans@095ab66)
#>  vctrs          0.6.5      2023-12-01 [1] CRAN (R 4.4.1)
#>  withr          3.0.2      2024-10-28 [1] CRAN (R 4.4.2)
#>  xfun           0.50       2025-01-07 [1] CRAN (R 4.4.2)
#>  yaml           2.3.10     2024-07-26 [1] CRAN (R 4.4.1)
#> 
#>  [1] C:/Users/Lauren/AppData/Local/R/win-library/4.4
#>  [2] C:/Program Files/R/R-4.4.2/library
#>  * ── Packages attached to the search path.
#> 
#> ──────────────────────────────────────────────────────────────────────────────
@malcolmbarrett
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This was already addressed in #22

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@LucyMcGowan @malcolmbarrett