Dear R users, I'm trying to specify a generalized linear mixed model in R, basically a Poisson model to describe monthly series of counts in different regions. My aim is to fit subject-specific curves, modelling a non-linear trend for each region through random effects for linear splines components (see Durban et al, Stat Med 2005, or " Semiparametric regression" by Ruppert et al, 2003). I use the command 'glmmPQL' in the MASS package and replicated the analysis with Stata's 'xtmepoisson'. I obtained very different results, so I would like to try 'glmer' in the lme4 package. I guess the default correlation for the random effects in 'glmer' is unstructured, but this choice is absolutely unfeasible for this complex random effect nesting structure. Unfortunately, I couldn't find a way to input simpler correlation structures (namely diagonal or identity), in the same way as the using the functions pdDiag or pdIdent with 'glmmPQL'. I wonder if this option is still to be implemented in lme4. In this case, any suggestion/comment? Thanks for your time
Antonio Gasparrini Public and Environmental Health Research Unit (PEHRU) London School of Hygiene & Tropical Medicine Keppel Street, London WC1E 7HT, UK Office: 0044 (0)20 79272406 - Mobile: 0044 (0)79 64925523 Skype contact: a.gasparrini http://www.lshtm.ac.uk/people/gasparrini.antonio ( http://www.lshtm.ac.uk/pehru/ ) ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
