 

Hi R users: I am analizing the leaf persistence during winter in seedlings
of a native tree species of Patagonia, I have just fitted an ordinal model
with a categorical response variable (3 levels), one fixed effect (with 4
levels) and one random effect nested within the fixed effect (with 10
levels), this is the model:

 

fm1<-clmm(leaf~population+(1|population:family),data=d,threshold"flexible",l
ink="probit",Hess=TRUE)

 

So I have 4 populations and 10 families within each population, the response
variable, leaf, has got 3 categories: A: 0-4 leaves, B: 5-15 leaves, C: 16
or more leaves. I have tested the significance of both effects using the LRT
method, and both have showed statistical significance, so far so good, but
now I want to asses differences within populations, as visual inspection of
the data suggests that one population has got more seedlings in the A
category than the other 3 populations. I am thinking in something like the
glht function of the multcomp package (tukey<-glht(fm1,
linfct=mcp(population="Tukey")), but obiously I cannot use the glht with a
clmm model, how could I asses these differences? 

Best regards

Fernando Barbero

PHD Candidate


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