2 correlated random effects with quadrature?
On 3/12/2013 2:18 PM, Ben Bolker wrote:
Ross Boylan <ross at ...> writes:
Is there a way to fit generalized linear mixed model with 2 correlated random effects in R, using quadrature? At the moment, I'm only concerned with binary outcomes. When I try glmer from lme4 with the quadrature argument I get Error: AGQ only defined for a single scalar random-effects term Yes, I know 2 dimensional quadrature is slow. Ross Boylaln
I don't know offhand of an R package that will do this. I'm pretty sure AS-REML uses PQL (not even Laplace approximation): AD Model Builder can only do GHQ for nested/grouped models (i.e. not crossed) with a single random effect per block.
I'm not sure if it matters, but the 2 random effects are both within the same cluster; they are for intercepts and slopes. The clusters themselves are not crossed or nested.
As far as I know you're simply out of luck:
Back to SAS nlmixed... For some reason I'm having trouble piping results from R to SAS on Linux.
both GHQ and the ability to handle crossed random effects are fairly rare among GLMM platforms, and the combination seems even rarer. I presume you've (1) compared Laplace approximation to GHQ with simpler examples and (2) compared Laplace approximation to 'truth' in simulations and found it wanting in one or both cases?
The main reason is that we want to compare the results with a more complicated model fit using 2-dimensional quadrature. The more complicated model, in R, takes into account the sampling scheme that generated the data, which we are simulating.
One alternativepossibility for improving the quality of the approximation would be to use importance sampling in AD Model Builder ...
Ross