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...3 9 7910.987 7962.567 -3946.494 2 vs 3 4.269119 0.1183 Thanks Dave 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 AI...

Dear R users, I have an unbalanced panel with an average of I=100 individuals and a total of T=1370 time intervals, i.e. T>>I. So far, I have been using the plm package. I wish to estimate a FE model like: res<-plm(x~c+v, data=pdata_frame, effect="twoways", model="within", na.action=na.omit) ?where c varies over i and t, and v represents an exogenous impact on x