I'm analysing my binomial dataset with R using a generalized linear mixed model (glmer, lme4-package). I wanted to make the pairwise comparisons of a certain fixed effect ("Sound") using a Tukey's post-hoc test (glht, multcomp-package).
Most of it is working fine, but one of my fixed effect variables ("SoundC") has no variance at all (96 times a "1" and zero times a "0") and it seems that the Tukey's test cannot handle that. All pairwise comparisons with this "SoundC" give a p-value of 1.000 whereas some are clearly significant.
As a validation I changed one of the 96 "1"'s to a "0" and after that I got normal p-values again and significant differences where I expected them, whereas the difference had actually become smaller after my manual change.
Does anybody have a solution? If not, is it fine to use the results of my modified dataset and report my manual change?
Reproducible example:
Response <- c(1,0,1,1,0,1,1,0,1,1,0,1,1,0,1,1,1,1,0,
0,1,1,0,1,1,0,1,1,0,1,1,0,1,1,0,1,1,0,1,1,0,
1,1,0,1,1,0,1,1,1,1,0,0,1,1,0,1,1,0,1,1,0,1,1,0,1)
Data <- data.frame(Sound=rep(paste0('Sound',c('A','B','C')),22),
Response,
Individual=rep(rep(c('A','B'),2),rep(c(18,15),2)))
# Visual
boxplot(Response ~ Sound,Data)
# Mixed model
library (lme4)
model10 <- glmer(Response~Sound + (1|Individual), Data, family=binomial)
# Post-hoc analysis
library (multcomp)
summary(glht(model10, mcp(Sound="Tukey")))
glm
model (which I bet you can) – Ben Bolker