Hi all,

I would like to perform a mixed effects regression as part of a
meta-analysis that I am working on, but I don't think that I can specify
the desired model properly using nlme and am unsure how to proceed using
MCMCglmm.

For this analysis, I am evaluating how plant communities  respond to
resource alteration along diversity gradients. Each observation is the log
response ratio of treatment/control in terms of biomass and corresponds to
a unique plant composition. For any given study, this yields multiple
observations per level of diversity, but I can't calculate standard error
or sampling variance for each (most have an n of 1).

Thus far, I have specified the following models in nlme:

fit<-lme(LRR~ Diversity, random=~1|Study/Mix, method="REML",data=bla) #
Diversity is numeric, Study is categorical,
                                                # and Mix is a categorical
representing each unique plant community

The model fit improves CONSIDERABLY when using varIdent , such that the
best fit model is:

fit2<-lme(LRR~ Diversity,
random=~1|Study/Mix,weights=varIdent(form=~1|Study)method="REML",data=bla)

The problem is that I would like to compare 'fit2'  to a model where
observations are weighted by the number of unique plant compositions in the
corresponding study, similar to what is done when using standard error or
sampling variance. In nlme, I had considered using:

what2<-varComb(varIdent(form=~1|Study),varFixed(~MixN))  # a combination of
variance functions, MixN is the number of unique mixtures for a given study

fit.4<-
lme(LRR~Diversity,data=bla,random=~1|Study/Mix,weights=what2,method="REML")

The resulting model, fit.4, has the same AIC, the same parameter estimates,
etc., leading me to conclude that the varComb  function is not having its
intended effect (as discussed here:
http://r.789695.n4.nabble.com/mixed-effects-meta-regression-nlme-vs-metafor-tt4656378.html#none
).

So, my question is, two-fold: a) how can I specify such a model that
weights studies by the number of unique plant compositions in nlme?  b) if
not a, what package would be best for this (MCMCglmm or metafor) and how
could it be implemented?

Many,many thanks for your help!

Cheers,

Dylan

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