> I am currently analyzing a dataset using lme(). The model I
> use has the following structure:
>
> model<-lme(response~Covariate+TreatmentA+TreatmentB,
> random=~1|Block/Plot,method="ML")
>
> When I plot the residuals against the fitted values, I see a clear
> positive trend (meaning that the variance increases with the mean).
>
> I tried to solve this issue using weights=varPower(), but it
> doesn�t change the residual plot at all.
You are aware that you need to use something like
weigths= varPower (form= fitted (.))
and the plot residuals using e.g.
scatter.smooth (fitted (model), resid (model, type= 'n'))
Maybe the latter should also be ok with the default pearson residuals, but I
am not sure.
Possibly a look into the following would help?
@Book{Pin:00a,
author = {Pinheiro, Jose C and Bates, Douglas M},
title = {Mixed-Effects Models in {S} and {S}-{P}{L}{U}{S}},
publisher = {Springer},
year = {2000},
address = {New York}
}
> How would you implement such a positive trend in the variance? I�ve
> tried glmmPQL (which works great with poisson errors), but
> using glmmPQL I can�t do model simplification.
Well, what error distribution do you have / do you expect?
Regards, Lorenz
-
Lorenz Gygax, Dr. sc. nat.
Centre for proper housing of ruminants and pigs
Swiss Federal Veterinary Office
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