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Greetings all The help file for GAMM in mgcv indicates that the log likelihood for a GAMM reported using summary(my.gamm$lme) (as an example) is not correct. However, in a past R-help post (included below), there is some indication that the likelihood ratio test in anova.lme(mygamm$lme, mygamm1$lme) is valid. How can I tell if anova.lme results are meaningful (are AIC, BIC, and logLik estimates accurate)? The data include hydroacoustic estimates of fish biomass (lbloat) in 1,000 meter long intervals (elementary sampling units) from multiple transects (each 20-30 km long, tranf) in...

...eatment). 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")...

Hi, I've an issue adding an interaction to a GAMM: My model was of form: gamm1 <- gamm(TOTSR ~ fROT + s(PH) + s(LOI) + s(ASP) + s(SQRT_ELEV) + CANCOV + s(SQRT_TOTCWD) + s(WELLF) + s(WELLN) + s(OLDWDLD) + s(DISTWOOD) + s(Annprec) + s(OLDWDLD:DISTWOOD) + (1|fSITE), family = poisson, data = BIOFOR2) with interaction of s(OLDWDLD:DISTWOOD). However I got this error message...

...ams I've always used the GCV scores to help with model selection, but I assume these aren't available for GAMMs estimated with REML. 2. Instead I've been using AIC values with GAMMs. Is it correct to use the AIC value from the underlying linear mixed model, i.e. extracted using summary(gamm1$mer)? Or is there another AIC calculated specifically for the gam object? Many thanks for help with these (admittedly simple and boring) questions, I really like the mgcv and gamm4 packages which I've found very user friendly in conjunction with Wood (2006). John [[alternative HTM...

...39;SB',]) However, this GAM shows heterogeneity in the residuals, and the model fit is not great. I would like to add a random effect to this GAM, for station (StnF), and a spatial correlation structure, for Depth_m. When trying to fit an equivalent GAMM to start with, I used the formula: SB.gamm1<-gamm(count~offset(vol_offset)+ s(Depth_m,by=StnF,bs="cs")+StageF*RegionF, family=negative.binomial(theta=1.41), niterPQL=50, data=Zoop_2011[Zoop_2011$SpeciesF=='SB',]) The theta=1.41 came from SB.gam4, using the theta.ml() command. Howeve...

....067646462 20.912908 2005 87.76123 2 527495.9 4843390 9 10 nn_1 0.000000 0.006012106 26.611785 2005 87.59767 2 527606.6 4842426 10 #GAM example bloat.gam8 <- gam(log10(yaobloaterbiom+0.00325) ~ lakef +s(depth, by=lakef), data=fish3) #GAMM example: bloat.gamm1 <- gamm(log10(yaobloaterbiom+0.00325) ~ lakef + s(depth, by=lakef), correlation=corAR1(form = ~ interval|tranf), data=fish3) However, GAMM results from models including a wide variety of correlation structures (corExp, CorSpher, CorLin, AR1, ARMA) produce autocorrelated residuals (similar l...

My GAMM model is to find drivers of species richness in forests is gamm1<- gamm(Total Species Richness ~ fROT + s(PH) + s(LOI) + ASP + s(SQRT_ELEV) + CANCOV + s(SQRT_TOTCWD) + s(WELLF) + s(WELLN) + s(OLDWDLD) + s(DISTWOOD) + s(Annprec), random=list (fSITE =~1), family = poisson, data = BIOFOR3) My issues are that the validation graphs are using methods I'm unfam...