<Antonio.Gasparrini <at> lshtm.ac.uk> writes: > 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'. >
1. I would suggest continuing this conversation on the r-sig-mixed-mod...@lists.r-project.org , which is specialized for this kind of question. 2. I don't think that a specific replacements for pdDiag/pdIdent are on their way any time soon, but you can get a diagonal structure for the random effects: see p. 16 of (broken URL so gmane doesn't complain about long lines ...) http://lme4.r-forge.r-project.org/slides/ 2009-07-21-Seewiesen/5LongitudinalD.pdf for an example. Ben Bolker ______________________________________________ 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.
