I'm not aware of any generic Adaptive Gaussian Quadrature (AGQ)
function currently available in R, similar to what you describe.
Certainly, 'nlme' can NOT do this. The lmer{lme4 / Matrix} and
glmmML{glmmML} packages both have specialized versions of this embedded
in their code but not extracted as a separate function, as far as I
know. I agree it would be nice to have such. However, I see two
problems with it.
First, we want to center the approximating Gaussian at some place
close to the maximum of the integrand over its variables of integration
given its parameters. One problem is that the quadrature points is a
function of this maximum, and the maximum is a function of the
parameters. Thus, we throw away potentially relatively expensive
function evaluations each time we change the center and scaling of the
approximating Gaussian. With a genuinely multidimensional integral,
this can become computationally quite expensive.
Second and often more important, AGQ will only perform well if the
ratio of the integrand to a normal density is adequately approximated by
a polynomial. If that's not the case, we do NOT get the accuracy
apparently promised by the Gaussian quadrature theorems. I've tried to
use AGQ for this type of problem and ultimately abandoned it because I
was not getting the accuracy I needed.
Besides this, any kind of adaptive integration is easier with
nested than crossed factors.
I've wanted to explore the possibilities of spline integration to
overcome both these problems, but I haven't had time to work on that.
This is not the answer you wanted, but I hope it helps.
Spencer Graves
Lei Liu wrote:
Hi there,
I am using SAS Proc NLMIXED to maximize a likelihood with
multivariate normal random effects. An example is the two part random
effects model for repeated measures semi-continous data with a
cluster at 0. I use the model y ~ general(loglike) statement in
Proc NLMIXED, so I can specify a general log likelihood function
constructed by SAS programming statements. Then the likelihood can be
maximized by AGQ. Is there a similar generic AGQ function in R to let
me write explicitly the log likelihood and then maximize it
accordingly? Can nlme do the work? Thanks!
Lei Liu
Assistant Professor
Division of Biostatistics and Epidemiology
Department of Public Health Sciences
School of Medicine
University of Virginia
3181 Hospital West Complex
Charlottesville, VA 22908-0717
1-434-982-3364 (o)
1-434-806-8086 (c)
1-434-243-5787 (f)
[EMAIL PROTECTED]
[EMAIL PROTECTED]
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