Dear Sirs, 

 

I'm a novel user of 'lme4' and 'languageR' packages, and not statistician,
and I am currently working with mixed models in population genetics. I have
fitted a very simple LMM, which has one fixed factor (population, with five
levels) and one random factor nested within population (family, 15 families
per population). My response variable is weight of a hundred seeds sample
and I have four samples per family: fm<-lmer
(w~1+population+(1|population:family)).

I used LRTs to test the significance of both factors (using REML and ML when
corresponding), which showed that both factors were significant. Till this
point everything is ok, but now I have the following doubt: I want to asses
differences between the levels of the fixed factor and I have two options,
the most simple would be to carry out a Tukey test with the multcomp package
(glht(model, linfct=mcp(population="Tukey"))), the other option is, and I am
not sure if it is plausible to do it, is to carry out a MCMC test.

Is it possible to assess the multiple differences between more than three
levels of a fixed factor in 'languageR' package with 'pvals.fnc'? To my
knowledge, performing one MCMC run allows to assess possible significant
differences between the first level and the rest.  

Can I assume that since MCMC is a semi-Bayesian approach it is possible to
perform separate runs?

I performed five different MCMC runs, in each one I modified the dataset so
as to have each population first in each run, so after performing the five
runs I had five different contrasts. In each, one of the five different
populations was compared against all the others, so I had all the contrasts
between populations. Is this way of analyzing my dataset ok? I understand
that the 'pvals.fnc' function is not intended to do this type of contrasts,
but I think what I have done is correct. I would like to know your point of
view regarding this.

 

Best regards

Fernando Barbero

PHD Candidate

 

 


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