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/ )
glmer (lme4), glmmPQL (MASS) and xtmepoisson (Stata)
2 messages · Antonio.Gasparrini at lshtm.ac.uk, Ben Bolker
<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-models at 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