heteroscedastic non-linear model with crossed random effects

Dear all

I am trying to model the relationship between the leaf area (lai) and  
total foliar nitrogen (tfn) in vegetation plots, in order to  
understand the sources of variation in tfn across sites and vegetation  
types.

lai and tfn was measured on each plot once, 300 plots in total, across 5 sites
and 4 vegetation types.

'Site' is therefore a factor from 1 to 5 and 'veg_type' is a factor  
from 1 to 4.

The theoretical relationship between lai and tfn is non-linear, of the form:

tfn = (No/g)*(1- exp(-g*lai))

where No and g are biologically meaningful parameters.

The most appropriate random effects structure for the model (I think)  
is to have crossed factors (vegetation types 1 to 4 all occurring at  
sites 1 to 5). The data are heteroscedastic with the variance of  
residuals increasing with the fitted values of tfn (though not for all  
groups).

My question is:

Is it possible to incorporate crossed factors in nlme? If so how?

Or, is it possible to incorporate the heteroscedasticity in nlmer? If so how?

I hope I've explained the problem clearly. I can find similar  
questions in the archives, but struggling to find a solution to this  
particular problem.

Any help much appreciated.

Lorna

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