I am aware this topic is not necessarily lmer() related but was hoping I could get some expert oppinion on this here nonetheless. I am trying to do model selection for quasipoisson GAMM using PQL and have the three models specified below. What method should I use to select the best model (note that ?Phase? has two levels)? gamm2<-gamm(response~s(SST)+as.factor(Month) + s(Long,Lat,by=as.factor(Phase))+offset(logArea),random=list(transect=~1), data=all.data,family=quasipoisson, niterPQL=40) gamm1<-gamm(response~ s(SST)+as.factor(Month)+s(Long,Lat)+as.factor(Phase)+offset(logArea), random=list(transect=~1), data=all.data,family=quasipoisson,niterPQL=40) gamm0<- gamm(response~s(SST)+as.factor(Month)+s(Long,Lat)+offset(logArea), random=list(transect=~1),data=all.data,family=quasipoisson,niterPQL=40) Each model has an $lme and a $gam object where the former is fitted using nlme. I have considered to compare AIC values from the $lme outputs but since the log-likelihood is not from the fitted GAMM I assume this is not the appropriate method. Crossvalidation would take too long for this study (simulation study with a large number of large data sets to which the models are fitted). The function gamm4::gamm4 uses lme4 instead of nlme (and avoids PQL). It does, however, not allow for specifying quasi-families and the data is overdispersed. I am aware that QAIC has been used for overdispersed GLM models but I cannot find anything that says this it is appropriate for overdispersed GLMM or GAMM. Many thanks and any help is much appreciated. Cheers, Cornelia <>< <>< <>< <>< <>< <>< <>< Cornelia Oedekoven CREEM University of St Andrews cornelia at mcs.st-and.ac.uk www.creem.st-and.ac.uk <>< <>< <>< <>< <>< <>< <>< The University of St Andrews is a charity registered in Scotland : No SC013532 ------------------------------------------------------------------ University of St Andrews Webmail: https://webmail.st-andrews.ac.uk
Model selection for mgcv::gamm (using PQL)
2 messages · Cornelia Oedekoven, David Duffy
On Tue, 6 Aug 2013, Cornelia Oedekoven wrote:
The function gamm4::gamm4 uses lme4 instead of nlme (and avoids PQL). It does, however, not allow for specifying quasi-families and the data is overdispersed.
I presume you could fit individual-level random effects in gamm4 (a la Scottish lip cancer etc). The MuMIn package seems to calculate AIC etc for gamm, so perhaps the author of that package would know the relevant literature? | David Duffy (MBBS PhD) ,-_|\ | email: davidD at qimr.edu.au ph: INT+61+7+3362-0217 fax: -0101 / * | Epidemiology Unit, Queensland Institute of Medical Research \_,-._/ | 300 Herston Rd, Brisbane, Queensland 4029, Australia GPG 4D0B994A v