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Reporting OR (exp(coef)) from glm with binomial link and glm predictions

Source: R/glm-utils.R
summaryGLM.Rd

Reporting OR from glm with binomial link and glm predictions

Usage

summaryGLM(object, id = NULL, fun = NULL, ...)

Arguments

object

glm output

id

possible id for cluster corrected standard errors

fun

possible function for non-standard predictions based on object

...

arguments of estimate of lava for example level=0.95

Author

Thomas Scheike

Examples

data(sTRACE)
sTRACE$id <- sample(1:100,nrow(sTRACE),replace=TRUE)

model <- glm(I(status==9)~sex+factor(diabetes)+age,data=sTRACE,family=binomial)
summaryGLM(model)
#> $coef
#>                   Estimate Std.Err     2.5%   97.5%   P-value
#> (Intercept)       -6.65169 0.82284 -8.26442 -5.0389 6.278e-16
#> sex                0.25832 0.22418 -0.18107  0.6977 2.492e-01
#> factor(diabetes)1  0.63305 0.30486  0.03553  1.2306 3.785e-02
#> age                0.09591 0.01099  0.07436  0.1175 2.707e-18
#> 
#> $or
#>                      Estimate         2.5%      97.5%
#> (Intercept)       0.001291843 0.0002575172 0.00648057
#> sex               1.294751030 0.8343769033 2.00914026
#> factor(diabetes)1 1.883341432 1.0361638586 3.42317957
#> age               1.100659282 1.0771942608 1.12463545
#> 
#> $fout
#> NULL
#> 
summaryGLM(model,id=sTRACE$id)
#> $coef
#>                   Estimate  Std.Err     2.5%   97.5%   P-value
#> (Intercept)       -6.65169 0.696211 -8.01623 -5.2871 1.246e-21
#> sex                0.25832 0.219869 -0.17262  0.6893 2.400e-01
#> factor(diabetes)1  0.63305 0.308637  0.02813  1.2380 4.026e-02
#> age                0.09591 0.009897  0.07651  0.1153 3.313e-22
#> 
#> $or
#>                      Estimate         2.5%       97.5%
#> (Intercept)       0.001291843 0.0003300608 0.005056214
#> sex               1.294751030 0.8414597074 1.992228761
#> factor(diabetes)1 1.883341432 1.0285304480 3.448585266
#> age               1.100659282 1.0795138629 1.122218896
#> 
#> $fout
#> NULL
#> 

nd <- data.frame(sex=c(0,1),age=67,diabetes=1)
predictGLM(model,nd)
#> $coef
#>                   Estimate Std.Err     2.5%   97.5%   P-value
#> (Intercept)       -6.65169 0.82284 -8.26442 -5.0389 6.278e-16
#> sex                0.25832 0.22418 -0.18107  0.6977 2.492e-01
#> factor(diabetes)1  0.63305 0.30486  0.03553  1.2306 3.785e-02
#> age                0.09591 0.01099  0.07436  0.1175 2.707e-18
#> 
#> $pred
#>     Estimate      2.5%     97.5%
#> p1 0.6004375 0.4494731 0.7344613
#> p2 0.6605188 0.5194651 0.7778730
#>