lmer option nAGQ=0
Hi Ben, The pdf file of lme4 speaks of "the penalized iteratively reweighted least squares step" and I was so naive as to think thought this would be the same as PQL. Of course, if this would have been so, it would have been explicitly mentioned there. Though I don't know what "conditional modes" are (and when these could be useful) it's good to know that it's NOT the same as pql! Sorry for the confusion, and thanks for solving this, Ben.
On 21-3-2014 14:12, Ben Bolker wrote:
On 14-03-21 07:13 AM, Ben Pelzer wrote:
Dear list, In R version 3.0.3, I recently udated the lme4 package and for a small dataset of N=108 pigsties, I ran logistic regression with a random intercept across the pigsties. The dependent variable is assumed to be binomially distributed, and represents the number of pigs in the sty that have a roundworm infection. There is a dichotomous predictor, denoting two different types of sty. Using the option nAGQ=0 produces PQL estimates. These estimates are, however, quite different from those obtained using sas (glimmix), spss (genlinmixed) and glmmPQL in R: these three routines produce very similar estimates of the two fixed and the one random effect. Now I'm wondering what the reason for the differences compared with lmer nAGQ=0 may be. The option to run PQL with lmer may be attractive if one has many (complex) models and large datasets, hence my questioning. Thanks for any help!! Ben.
It's not true that AGQ=0 produces PQL estimates (so it's not at all surprising that the results don't match the results of PQL estimates). Rather, it produces 'conditional estimates' -- it estimates the conditional modes, but doesn't apply a Laplace approximation (or any other). Don't have time to say more now, sorry. Ben Bolker