Dear Jamovi mavens,
I'm a new user and delighted that Jamovi exists. I loaded the gamlj module because I need to analyze overdispersed count data while also modeling random effects associated with a cluster variable ("ID" in my R example below), where "a" and "b" are two levels of a factor "name" and "counts" are the counts.
Among the tools I can see Poisson overdispersion can be modeled in the Generalized Linear Models interface, and random effects on a cluster variable can be modeled in Generalized Mixed Models, but I cannot see how to put them both in the same model.
If it helps, my R code for a simple model using the lme4 package looks something like below but I have others with multiple factors with multiple levels - love the jamovi interface for that.
Thanks,
onyaw
a<-1*(data$name=='A')
b<-1*(data$name=='B')
counts_d<-data$counts
rep_ind<-c(1:length(a))
ID_e<-(data$ID)
#FIT FULL MODEL WITH INTERACTION TERMS
fit_a<-glmer(counts_d~(1|rep_ind) +(1|ID_e) + b, family='poisson')
#FIT NULL MODEL
fit_a0<-glmer(counts_d~(1|rep_ind) +(1|ID_e), family='poisson')
lr_a<- as.numeric(-2*(logLik(fit_a0)-logLik(fit_a)))
p_a<-exp(pchisq(lr_a,1,lower.tail=F,log.p=T)) #P-VALUE FOR INTERACTION TEST
p_a
how to model random effects AND overdispersion in GLM
- mcfanda@gmail.com
- Posts: 462
- Joined: Thu Mar 23, 2017 9:24 pm
Re: how to model random effects AND overdispersion in GLM
Hi, generalized mixed models are new in the gamlj menu, and for the moment mixed overdispersed logistic models are not implemented. You can run analyses implemented in your R code with gamlj, but not the overdispersion, yet. It's in the pipeline and it would come out soon