Closed danielebottigliengo closed 3 years ago
Thank you so much for letting me know about this! It should e fixed now.
The problem was due to WegithIt
splitting the race
factor variable into three categories and supplying all of them to CBPS()
, which meant the design matrix was not full rank, leading to erratic behavior. You can actually replicate this using CBPS()
in the following way:
lalonde_split <- cobalt::splitfactor(lalonde, drop.first = FALSE)
ps_1 <- CBPS(
treat ~ age + educ + race_black + race_hispan + race_white + married + nodegree + re74 + re75,
data = lalonde_split, ATT = 0
)
ps_2 <- CBPS(
treat ~ educ + race_black + race_hispan + race_white + nodegree + re74 + re75 + married + age,
data = lalonde_split, ATT = 0
)
head(data.frame(
ps1 = ps_1$fitted.values,
ps2 = ps_2$fitted.values
))
I've fixed weightit()
so that it ensures the matrix is full rank, yielding the same propensity scores every time.
Thank you so much @ngreifer for your quick answer!
Hi!
Thanks for the great package!
I have noticed that when
method = "cbps"
is used in theweightit
function different PSs values are returned if the covariates on the right-hand side of the formula are specified in different orders. However, if PSs are estimated "outside" of theweightit
function usingCBPS::CBPS
they look identical. Here's a reproducible example usinglalonde
dataset.Created on 2020-12-28 by the reprex package (v0.3.0)
Session info
``` r devtools::session_info() #> - Session info --------------------------------------------------------------- #> setting value #> version R version 4.0.3 (2020-10-10) #> os Windows 10 x64 #> system x86_64, mingw32 #> ui RTerm #> language (EN) #> collate Italian_Italy.1252 #> ctype Italian_Italy.1252 #> tz Europe/Berlin #> date 2020-12-28 #> #> - Packages ------------------------------------------------------------------- #> package * version date lib source #> assertthat 0.2.1 2019-03-21 [1] CRAN (R 4.0.0) #> backports 1.2.1 2020-12-09 [1] CRAN (R 4.0.3) #> callr 3.5.1 2020-10-13 [1] CRAN (R 4.0.3) #> CBPS * 0.21 2019-08-21 [1] CRAN (R 4.0.0) #> cli 2.2.0 2020-11-20 [1] CRAN (R 4.0.3) #> codetools 0.2-16 2018-12-24 [2] CRAN (R 4.0.3) #> colorspace 2.0-0 2020-11-11 [1] CRAN (R 4.0.3) #> crayon 1.3.4 2017-09-16 [1] CRAN (R 4.0.0) #> desc 1.2.0 2018-05-01 [1] CRAN (R 4.0.0) #> devtools 2.3.2 2020-09-18 [1] CRAN (R 4.0.2) #> digest 0.6.27 2020-10-24 [1] CRAN (R 4.0.3) #> dplyr 1.0.2 2020-08-18 [1] CRAN (R 4.0.2) #> ellipsis 0.3.1 2020-05-15 [1] CRAN (R 4.0.0) #> evaluate 0.14 2019-05-28 [1] CRAN (R 4.0.0) #> fansi 0.4.1 2020-01-08 [1] CRAN (R 4.0.0) #> foreach 1.5.1 2020-10-15 [1] CRAN (R 4.0.3) #> fs 1.5.0 2020-07-31 [1] CRAN (R 4.0.2) #> generics 0.1.0 2020-10-31 [1] CRAN (R 4.0.3) #> ggplot2 3.3.2 2020-06-19 [1] CRAN (R 4.0.2) #> glmnet * 4.0-2 2020-06-16 [1] CRAN (R 4.0.2) #> glue 1.4.2 2020-08-27 [1] CRAN (R 4.0.2) #> gtable 0.3.0 2019-03-25 [1] CRAN (R 4.0.0) #> highr 0.8 2019-03-20 [1] CRAN (R 4.0.0) #> htmltools 0.5.0 2020-06-16 [1] CRAN (R 4.0.2) #> iterators 1.0.13 2020-10-15 [1] CRAN (R 4.0.3) #> knitr 1.30 2020-09-22 [1] CRAN (R 4.0.2) #> lattice 0.20-41 2020-04-02 [2] CRAN (R 4.0.3) #> lifecycle 0.2.0 2020-03-06 [1] CRAN (R 4.0.0) #> magrittr 2.0.1 2020-11-17 [1] CRAN (R 4.0.3) #> MASS * 7.3-53 2020-09-09 [2] CRAN (R 4.0.3) #> MatchIt * 4.1.0 2020-12-15 [1] CRAN (R 4.0.3) #> Matrix * 1.2-18 2019-11-27 [2] CRAN (R 4.0.3) #> memoise 1.1.0 2017-04-21 [1] CRAN (R 4.0.0) #> munsell 0.5.0 2018-06-12 [1] CRAN (R 4.0.0) #> nnet * 7.3-14 2020-04-26 [2] CRAN (R 4.0.3) #> numDeriv * 2016.8-1.1 2019-06-06 [1] CRAN (R 4.0.0) #> pillar 1.4.7 2020-11-20 [1] CRAN (R 4.0.3) #> pkgbuild 1.2.0 2020-12-15 [1] CRAN (R 4.0.3) #> pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.0.0) #> pkgload 1.1.0 2020-05-29 [1] CRAN (R 4.0.0) #> prettyunits 1.1.1 2020-01-24 [1] CRAN (R 4.0.0) #> processx 3.4.5 2020-11-30 [1] CRAN (R 4.0.3) #> ps 1.5.0 2020-12-05 [1] CRAN (R 4.0.3) #> purrr 0.3.4 2020-04-17 [1] CRAN (R 4.0.0) #> R6 2.5.0 2020-10-28 [1] CRAN (R 4.0.3) #> Rcpp 1.0.5 2020-07-06 [1] CRAN (R 4.0.2) #> remotes 2.2.0 2020-07-21 [1] CRAN (R 4.0.2) #> rlang 0.4.9 2020-11-26 [1] CRAN (R 4.0.3) #> rmarkdown 2.6 2020-12-14 [1] CRAN (R 4.0.3) #> rprojroot 2.0.2 2020-11-15 [1] CRAN (R 4.0.3) #> scales 1.1.1 2020-05-11 [1] CRAN (R 4.0.0) #> sessioninfo 1.1.1 2018-11-05 [1] CRAN (R 4.0.0) #> shape 1.4.5 2020-09-13 [1] CRAN (R 4.0.2) #> stringi 1.5.3 2020-09-09 [1] CRAN (R 4.0.2) #> stringr 1.4.0 2019-02-10 [1] CRAN (R 4.0.0) #> survival 3.2-7 2020-09-28 [2] CRAN (R 4.0.3) #> testthat 3.0.1 2020-12-17 [1] CRAN (R 4.0.3) #> tibble 3.0.4 2020-10-12 [1] CRAN (R 4.0.3) #> tidyselect 1.1.0 2020-05-11 [1] CRAN (R 4.0.0) #> usethis 2.0.0 2020-12-10 [1] CRAN (R 4.0.3) #> vctrs 0.3.6 2020-12-17 [1] CRAN (R 4.0.3) #> WeightIt * 0.10.2 2020-08-21 [1] Github (ngreifer/WeightIt@7811149) #> withr 2.3.0 2020-09-22 [1] CRAN (R 4.0.3) #> xfun 0.19 2020-10-30 [1] CRAN (R 4.0.3) #> yaml 2.2.1 2020-02-01 [1] CRAN (R 4.0.0) #> #> [1] C:/Users/Daniele/Documents/R/win-library/4.0 #> [2] C:/Program Files/R/R-4.0.3/library ```I also tried using
method = "ps"
andmethod = "gbm"
, but I didn't notice any differences in the PSs values. I wasn't expecting different PSs values when covariates are specified in different orders, but maybe there is something that I am missing.Thank you very much!