Dear R-sig-mixed-models,

My colleague and I are trying to verify results of running hierarchical generalized linear models in STATA and R.  In STATA to designate the number of trials we used binomial(10). To do this in R we created a variable that was 10 for every observation call trial.  We ran the model two ways in R with and without the offset command. The following are our commands in STATA and R respectively:

Xtmelogit dv classb trt_lam, || id_lam: trt_lam, covariance(unstructured) binomial(10)

glm22 <- glmmadmb(formula=dv~trt.f + classb.f + offset(trial) + (trt.f|id.f), data=lam_hlm, family="binomial", link="logit",corStruct="full")

glm_off <- glmmadmb(formula=dv~trt.f + classb.f + (trt.f|id.f), data=lam_hlm, family="binomial", link="logit",corStruct="full")

Our results for the fixed effects, random effects and log likelihood are different between R and STATA for both R models. We are not sure why they are so different and were wondering if this issue has been encountered before and if additional assistance could help explain this.

Emily Price and Christine Crumbacher

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