[R-sig-ME] lmer option nAGQ=0

[Ben Bolker]

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