Dear all, I’m analyzing this dataset containing biodiversity indices, measured over time (Week), and at various contaminant concentrations (Treatment). We have two replicates (Replicate) per treatment. I’m looking for the effects of time (Week) and contaminant concentration (Treatment) on diversity indices (e.g. richness).
Initial analysis with GAM models showed temporal autocorrelation of diversity. So now I’m trying to fit this gamm (gamm1): gamm1 <- gamm(richness~ s(Week,by=as.numeric(Treatment=="0"),k=6) + s(Week,by=as.numeric(Treatment=="0.5"),k=6) + s(Week,by=as.numeric(Treatment=="5"),k=6) + s(Week,by=as.numeric(Treatment=="15"),k=6) + s(Week,by=as.numeric(Treatment=="50"),k=6) + s(Week,by=as.numeric(Treatment=="150"),k=6) + s(Treatment,k=6,fx=FALSE) + factor(Treatment), correlation=corCAR1(form=~Week|factor(Treatment),data=indices,family=gaussian) I seem to be having difficulties with the correlation structure. An initial error occurred because replicates were taken at the same time: /Error in Initialize.corCAR1(X[[2L]], ...) : Covariate must have unique values within groups for corCAR1 objects / I solved this by selecting one replicate but is there another solution for this? Moreover, when analyzing the data of one replicate, I received following error: /Error in MEestimate(lmeSt, grps) : Singularity in backsolve at level 0, block 1 / I have no idea how to solve this. It seems to be related with the complexity of the model because no error occurred when running a simpler gamm (gamm2): gamm2 <- gamm(richness~ s(Week,k=6,fx=FALSE) + factor(Treatment), correlation=corCAR1(form=~Week|conc.f), data=test,family=gaussian) Any help would be well appreciated! With kind regards, Karel -- View this message in context: http://r.789695.n4.nabble.com/gamm-problems-with-corCAR1-tp3874669p3874669.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
