Dealing with heteroscedasticity in a GLM/M

This is, strictly speaking, the wrong approach, but in order to explore the 
presence of heteroscedasticity, you could try to use the linear ME functions and 
the variance objects in that.

What I suggest by way of exploration is the following. If you regress a binomial 
response as if it were a countinuous variable in a standard OLS regression 
setting many problems arise,  including out of unit interval predictions and the 
error term is heteroscedastic. That heteroscedasticity is of a known form 
however, the variance being p*(1-p) where p is x*b is the linear predictor of 
the probability.

I would suggest you compare two models, both estimated using lme in the nlme 
package. One which models the response and includes a variance function that 
takes into account the heteroscedasticity induced by having a binary rather than 
continuous dependent variable. You then compare that with a model that adds, 
using the varComb() function, the heteroscedasticity you worry about.

Markus

Dealing with heteroscedasticity in a GLM/M

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