[R] lme and varFunc()

[Lorenz.Gygax@fat.admin.ch]

> 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