Dear all,
I am expecting a Poisson error distribution in my lme with weights=varFunc().
The "weigths= varPower (form= fitted (.))" doesnīt work due to missing values in the response:
Problem in lme.formula(fixed = sqrt(nrmainaxes + 0...: Maximum number of iterations reached without convergence. Use traceback() to see the call stack
Thatīs why Iīve used one of my most important explanatory variables as a variance covariate:
weigths= varPower (form=~explanatory)
With that, it worked out properly so far.
What would your suggestion in such a case be?
Regards Christoph
[EMAIL PROTECTED] wrote:
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
doesnīt 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? Iīve tried glmmPQL (which works great with poisson errors), but using glmmPQL I canīt 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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