Hello all, I have been working with data from 8 different tree populations
of an american beech, currently I have been recording the filling of seeds
(binary response variable)and have fitted a GLMM with lme4 which has this
structure:

 

model<-lmer(
seedfilling~population+(1|population:tree)+typewing+typewing*population,fami
ly=binomial,data1)

 

where:

seedfilling is the binary response variable (1= filled seed,0=empty seed)

population is a fixed effect of population, I have eight different
populations

tree is a random effect nested under population (I harvested 15 trees
randomly chosen in every population)

typewing is the type of wing, as seeds in this tree species can have 2 or 3
wings

typewing * population is an interaction effect

I recorded the filling of 150 seeds in every tree

 

So far so cool. I have tested the significance of all effects with LRTs and
they all showed to be significant, now I want to study if there are any
differences between populations (If I had a linear mixed model I would be
using the glht function of Multcomp package). Colleagues that work with SAS
have told me, and in fact they have carried out,  this analysis using the
"slice" command but as I work with R I want to do it with it, do you know if
this can be carried out?

Best regards

Fernando


	[[alternative HTML version deleted]]


