This comparison is just as valid as it is for a regular linear mixed model,
which is all that the GAMM is in this case --- the smoothing parameters are
just variance components in your example.
In general you have to be a bit careful with generalized likelihood ratio
tests involving variance
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
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