title: Post-hoc test for glmer
body: <p>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).</p>
  
  <p>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.</p>
  
  <p>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.</p>
  
  <p>Does anybody have a solution? If not, is it fine to use the results of my modified dataset and report my manual change?</p>
  
  <p>Reproducible example:</p>
  
  <pre><code>Response &lt;- 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 &lt;- 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 &lt;- glmer(Response~Sound + (1|Individual), Data, family=binomial)
  
  # Post-hoc analysis
  library (multcomp)
  summary(glht(model10, mcp(Sound="Tukey")))
  </code></pre>