Dear R-expert
I'm fitting a non linear model (energy allocation model to individual
growth data) using your nlme routine. For each individual I have thus a
number of observations (age and size) to which I fit the nonlinear
function, with random effects for the individuals on the estimated
parameters (individual as the grouping factor). The sampling of these
individuals was stratified (size stratified) and the observations are
thus not representative for the population. But as we know the true size
distributions over the strata, we can compute a statistical weight for
each individual, given by the frequency of the size at age of that
individual in the true population distribution. To obtain representative
estimates in the nlme, I would therefore preferably weight the fitting
by these statistical weights. In each group (which is the individual) a
different weighting factor would apply (I guess that the individual
estimation will not be much affected by these weights, but the
population mean). I don't quite see how to do this weighting by
nlme-group.
I think what I need is something that multiplies these weights to the
residual variance. My first hint would be something as it is described
by the function varIdent or varFixed, but it is not quite clear to me
what is being done by these (e.g. what is meant by variance covariate
etc.?).
I thank you very much in advance if you could briefly comment on that
and point out the function for the weighting that should be applied.
All the best
Fabian Mollet
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