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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()
#> R version 4.3.2 (2023-10-31)
#> Platform: aarch64-apple-darwin22.6.0 (64-bit)
#> Running under: macOS Sonoma 14.3.1
#>
#> Matrix products: default
#> BLAS: /Users/kkzh/.asdf/installs/R/4.3.2/lib/R/lib/libRblas.dylib
#> LAPACK: /Users/kkzh/.asdf/installs/R/4.3.2/lib/R/lib/libRlapack.dylib; LAPACK version 3.11.0
#>
#> locale:
#> [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
#>
#> time zone: Europe/Copenhagen
#> tzcode source: internal
#>
#> 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] cli_3.6.2 knitr_1.45 rlang_1.1.3
#> [4] xfun_0.41 highr_0.10 jsonlite_1.8.8
#> [7] listenv_0.9.1 future.apply_1.11.1 lava_1.7.4
#> [10] htmltools_0.5.6.1 sass_0.4.7 rmarkdown_2.25
#> [13] grid_4.3.2 evaluate_0.23 jquerylib_0.1.4
#> [16] fastmap_1.1.1 mvtnorm_1.2-4 yaml_2.3.7
#> [19] numDeriv_2016.8-1.1 compiler_4.3.2 codetools_0.2-19
#> [22] ucminf_1.2.0 Rcpp_1.0.12 future_1.33.1
#> [25] lattice_0.22-5 digest_0.6.34 R6_2.5.1
#> [28] parallelly_1.37.0 parallel_4.3.2 splines_4.3.2
#> [31] bslib_0.5.1 Matrix_1.6-5 tools_4.3.2
#> [34] globals_0.16.2 cachem_1.0.8
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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