MCMCglmm in the package of the same name might be able to help...it could
also be that glmmADMB has this functionality.

HTH

Alan



--------------------------------------------------
Email: aghaynes@gmail.com
Mobile: +41794385586
Skype: aghaynes


On 23 August 2012 07:58, Leila Brook <leila.brook@my.jcu.edu.au> wrote:

> I am hoping to find a way to account for heterogeneity of variance between
> categories of explanatory variables in a generalised model.
>
> I have searched books and this forum, and haven't found any advice that I
> understood could help account for this assumption in a generalised model
> context, as I can't fit a variance structure in lme4.
>
>
>
> As background to my study:
>
> I used camera stations set up in pairs (one positioned on a track and one
> off the track) to record my study species, and used the same pairs in each
> of two seasons. As my surveys were repeated, I have specified camera pair
> as a random effect. I am using a binomial model in lme4 to model the
> proportion of nights an animal was recorded, as a function of the fixed
> effects of season (2 categories), position (categorical: whether on or off
> the track), area (categorical: one of two areas) and continuous habitat
> variables, plus interactions between them.
>
>
>
> I validated the GLM form of the model, including plotting the deviance
> residuals against my explanatory variables, and have noted that the
> variance of residuals for the categorical variables appears to differ.
>
> Differences in the response variable between these categories were part of
> my research question and are evident in the raw data, so I don't want to
> remove them from the analysis.
>
> I have tried converting my response variable to be continuous, but when I
> do, it is not normal (too many zeros), nor is the log transformed variable.
>
>
>
> I have read that nlme can fit a variance structure to a LME, but haven't
> heard of a way of dealing with heterogeneity in a generalised mixed effect
> model, nor found an R package that can fit a variance structure to a GLMM.
>
> Can anyone provide any advice on how to overcome this problem, or whether
> I can continue, with some form of caveat, with the GLMM?
>
>
>
> Thanks in advance,
>
> Leila
>
>
>
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