Dear all,
Regarding the lme with varFunc() question I posted a few days ago: I have used the following two approaches:
model1<-lme(response~Covariate+Block+TreatmentA+TreatmentB,random=~1|Plot/Subplot,method="ML") model2a<-update(model1,weights=varPower(form=~ fitted(.))) model2b<-update(model1,weights=varPower(form=~block))
While model2a produces an error
"Problem in .C("mixed_loglik",: subroutine mixed_loglik: Missing values in argument 1 Use traceback() to see the call stack"
Model 2b seems to work fine, now.
Iīm not sure why model2a doesnīt work, but using an important explanatory variable (block) as a variance covariate seems to do a better job (although I donīt really understand why)
Does anyone have an explanation for this?
Regards, Chris.
Andrew Robinson wrote:
Dear Christoph,
what command are you using to plot the residuals? If you use the default residuals it will not reflect the variance model. If you use the argument
type="p"
then you get the Pearson residuals, which will reflect the weights model. Try something like this:
plot(model, resid(., type = "p") ~ fitted(.), abline = 0)
I hope that this helps,
Andrew
On Mon, Jan 24, 2005 at 02:28:44PM +0100, Christoph Scherber wrote:
Dear R users,
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.
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.
Many thanks for your help!
Regards Chris.
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