GEE cluster standard errors and predictions for glm objects

Utility functions for GLM objects

Getting the OR with confidence intervals using the GEE (sandwhich) standard errors

set.seed(100)

library(mets)
data(bmt); 
bmt$id <- sample(1:100,408,replace=TRUE)

glm1 <- glm(tcell~platelet+age,bmt,family=binomial)
summaryGLM(glm1)
#> $coef
#>             Estimate Std.Err    2.5%   97.5%   P-value
#> (Intercept)  -2.4371  0.2225 -2.8732 -2.0009 6.481e-28
#> platelet      1.1368  0.3076  0.5340  1.7397 2.189e-04
#> age           0.5927  0.1551  0.2888  0.8966 1.319e-04
#> 
#> $or
#>               Estimate       2.5%     97.5%
#> (Intercept) 0.08741654 0.05651794 0.1352076
#> platelet    3.11688928 1.70573194 5.6955015
#> age         1.80895115 1.33489115 2.4513641
#> 
#> $fout
#> NULL

## GEE robust standard errors
summaryGLM(glm1,id=bmt$id)
#> $coef
#>             Estimate Std.Err    2.5%   97.5%   P-value
#> (Intercept)  -2.4371  0.2157 -2.8599 -2.0142 1.361e-29
#> platelet      1.1368  0.2830  0.5822  1.6914 5.877e-05
#> age           0.5927  0.1434  0.3117  0.8738 3.568e-05
#> 
#> $or
#>               Estimate       2.5%     97.5%
#> (Intercept) 0.08741654 0.05727471 0.1334211
#> platelet    3.11688928 1.79006045 5.4271903
#> age         1.80895115 1.36575550 2.3959664
#> 
#> $fout
#> NULL

Predictions also simple

age <- seq(-2,2,by=0.1)
nd <- data.frame(platelet=0,age=seq(-2,2,by=0.1))
pnd <- predictGLM(glm1,nd)
head(pnd$pred)
#>      Estimate       2.5%      97.5%
#> p1 0.02601899 0.01115243 0.05951051
#> p2 0.02756409 0.01214068 0.06136414
#> p3 0.02919819 0.01321187 0.06328733
#> p4 0.03092608 0.01437206 0.06528441
#> p5 0.03275278 0.01562757 0.06736019
#> p6 0.03468351 0.01698493 0.06952008
plot(age,pnd$pred[,1],type="l",ylab="predictions",xlab="age",ylim=c(0,0.3))
matlines(age,pnd$pred[,-1],col=2)

SessionInfo

sessionInfo()
#> R version 4.3.2 (2023-10-31)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: Ubuntu 22.04.3 LTS
#> 
#> Matrix products: default
#> BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0
#> 
#> locale:
#>  [1] LC_CTYPE=C.UTF-8       LC_NUMERIC=C           LC_TIME=C.UTF-8       
#>  [4] LC_COLLATE=C.UTF-8     LC_MONETARY=C.UTF-8    LC_MESSAGES=C.UTF-8   
#>  [7] LC_PAPER=C.UTF-8       LC_NAME=C              LC_ADDRESS=C          
#> [10] LC_TELEPHONE=C         LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C   
#> 
#> time zone: UTC
#> tzcode source: system (glibc)
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#> [1] mets_1.3.4     timereg_2.0.5  survival_3.5-7
#> 
#> loaded via a namespace (and not attached):
#>  [1] Matrix_1.6-1.1      future.apply_1.11.1 jsonlite_1.8.8     
#>  [4] highr_0.10          compiler_4.3.2      Rcpp_1.0.12        
#>  [7] stringr_1.5.1       parallel_4.3.2      jquerylib_0.1.4    
#> [10] globals_0.16.2      splines_4.3.2       systemfonts_1.0.5  
#> [13] textshaping_0.3.7   yaml_2.3.8          fastmap_1.1.1      
#> [16] lattice_0.21-9      R6_2.5.1            knitr_1.45         
#> [19] future_1.33.1       desc_1.4.3          bslib_0.6.1        
#> [22] rlang_1.1.3         cachem_1.0.8        stringi_1.8.3      
#> [25] xfun_0.42           fs_1.6.3            sass_0.4.8         
#> [28] memoise_2.0.1       cli_3.6.2           pkgdown_2.0.7      
#> [31] magrittr_2.0.3      digest_0.6.34       grid_4.3.2         
#> [34] mvtnorm_1.2-4       lifecycle_1.0.4     lava_1.7.3         
#> [37] vctrs_0.6.5         evaluate_0.23       glue_1.7.0         
#> [40] listenv_0.9.1       numDeriv_2016.8-1.1 codetools_0.2-19   
#> [43] ragg_1.2.7          parallelly_1.37.0   rmarkdown_2.25     
#> [46] purrr_1.0.2         tools_4.3.2         htmltools_0.5.7

Klaus Holst & Thomas Scheike

2024-02-20

Utility functions for GLM objects

SessionInfo