R help - Nov 2012 - glht function in multcomp gives unexpected p=1 for all comparisons

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?