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...message: Some predictor variables are on very different scales: consider rescaling # plot(model$gam, page=1, font.lab=2, xlab="Residual Q95") M <-predict(model$gam,type="response",se.fit=T) # joining the dataset and the predictions keep it "in house" and on path springP = cbind(spring, M) springP$upper = with(springP,fit+1.96*se.fit) springP$lower = with(springP,fit-1.96*se.fit) str(springP) 'data.frame': 11744 obs. of 15 variables: $ WATERBODY_ID : Factor w/ 1994 levels "GB102021072830",..: 1 1 2 2 2 3 3 3 4 4 ... $ SITE_ID...

Dear all,? I hope that you can help me on this. I have been struggling to figure this out but I haven't found any solution. I am running a generalised mixed effect model, gamm4, for an ecology project. Below is the code for the model: model<-gamm4(LIFE.OE_spring~s(Q95, by=super.end.group)+Year+Hms_Rsctned+Hms_Poaching+X.broadleaved_woodland? ? ? ? ? ? ?+X.urban.suburban+X.CapWks,