GibsonR <rachel.gibson <at> bristol.ac.uk> writes:
>
> My data have heterogeneity of variance (in a categorical variable), do I need
> to specify a variance structure accounting for this in my model or do GLMMs
> by their nature account for such heterogeneity (as a result of using
> deviances rather than variances)? And if I do need to do this, how do I do
> it (e.g. using something like the VarIdent function in nlme) and in what
> package?
We need a little more information.
Also, it might be better to send follow-ups to r-sig-mixed-models <at>
r-project.org .
Is your a categorical variable a predictor ("independent") or
response ("dependent") variable? If it's a predictor, then the
details of its distribution are not important for the validity
of the analysis. It it's a response, then you need to be doing
a multinomial or ordinal response model. GLMs and GLMMs do account
for some forms of heterogeneity in variance.
You probably need to tell us more about what you tried to do
and how you concluded that heteroscedasticity was a problem.
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