[R] glht function in multcomp gives unexpected p=1 for all comparisons

[Katie Boothroyd-Roberts]

Hi, I have data with binomial response variable (survival) and 2 categorical independent variables (site and treatment) (see below).  I have run a binomial GLM and found that both IVs and the interaction are significant.  Now I want to do a post-hoc test for all pairwise comparisons to see which treatment groups differ.  I tried the glht function in the multcomp package, but I get surprising results, with p=1 for all comparisons.  

# My data: 
surv.data <- data.frame(
Site=c(rep("Site1", 9), rep("Site2", 9)), 
Treatment=rep(c(rep("Treat1", 3), rep("Treat2", 3), rep("Treat3", 3)), 2),
survival=c(0.9, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.8, 0.4, 0.6, 0.5, 1.0, 0.7, 0.2, 0.2, 0.4)
)

# Binomial GLM:
glm.out <- glm(survival~Site*Treatment, data=surv.data, family="binomial", weights=rep(10, nrow(surv.data)))
anova(glm.out, test="Chisq")    # Treatment effect: p=0.001291

# Post-hoc test
library(multcomp)
summary(glht(glm.out, mcp(Treatment="Tukey", interaction_average=TRUE)))



This gives me the following results:

         Simultaneous Tests for General Linear Hypotheses

Multiple Comparisons of Means: Tukey Contrasts


Fit: glm(formula = survival ~ Site * Treatment, family = "binomial", 
    data = surv.small, weights = rep(10, nrow(surv.small)))

Linear Hypotheses:
                        Estimate Std. Error z value Pr(>|z|)
Deltoide - Baumier == 0   -9.179   2133.341  -0.004        1
Forest - Baumier == 0     -1.012   3016.999   0.000        1
Forest - Deltoide == 0     8.167   2133.341   0.004        1
(Adjusted p values reported -- single-step method)

Can anyone explain to me why p=1 for all comparisons even though the GLM showed that the treatment main effect was highly significant?

I searched in the archives and found two similar questions, but without helpful answers:
http://r.789695.n4.nabble.com/glht-problem-tt890671.html
http://r.789695.n4.nabble.com/glht-multicomparisons-with-a-binomial-response-variable-tt4360898.html

Thank-you in advance!

Katie