Hi R user,
I am using the gamm() function of the mgcv-package. Now I would like to
decide on the random effects to include in the model. Within a GAMM
framework, is it allowed to compare the following two models
inv_1<-gamm(y~te(sat,inv),data=daten_final, random=list(proband=~1))
inv_2<-gamm(y~te(sat,inv),data=daten_final, random=list(proband=~sat))
with a likelihood ratio test for a traditional GLMM, like this:
anova(inv_1$lme, inv_2$lme)
The output is as follows:
Model df AIC BIC logLik Test L.Ratio p-value
inv_2$lme 1 10 21495.90 21557.59 -10737.95
inv_1$lme 2 8 23211.12 23260.46 -11597.56 1 vs 2 1719.214 <.0001
Or is this not in tune with the automatic smoothing parameter selection
(i.e. it is not exactly the same for both model alternatives)?
If not, how can I decide on the selection of random effects?
Thanks in advance for your help.
Best regards
Maik
Dipl.-Kfm. Maik Eisenbeiß
Marketing Centrum Münster
Institut für Anlagen und Systemtechnologien
Westfälische Wilhelms-Universität Münster
Am Stadtgraben 1315
48143 Münster
Telefon: +49 251 83-29920
Telefax: +49 251 83-22903
E-Mail: [EMAIL PROTECTED]
Web: http://www.marketing-centrum.de/ias
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