[R] contrast lme and glmmPQL and getting additional results...

[kjetil@entelnet.bo]

On 20 Mar 2004 at 11:06, Paul Johnson wrote:

>From what you say, it seems like you have a linear normal (mixed) 
model. This is what lme is made for, and there is no reason to use 
glmmPQL (which calls lme iteratively).

Kjetil Halvorsen

> I have a longitudinal data analysis project.  There are 10
> observations on each of 15 units, and I'm estimating this with
> randomly varying intercepts along with an AR1 correction for the error
> terms within units.  There is no correlation across units.  Blundering
> around in R for a long time, I found that for linear/gaussian models,
> I can use either the MASS method glmmPQL (thanks to Venables and
> Ripley) or the lme from nlme (thanks to Pinheiro and Bates).  (I also
> find that the package lme4 has GLMM, but I can't get the correlation
> structure to work with that, so I gave up on that one.)
> 
> The glmmPQL and lme results are quite similar, but not identical.
> 
> Here are my questions.
> 
> 1. I believe that both of these offer consistent estimates. Does one
> have preferrable small sample properties?  Is the lme the preferred
> method in this case because it is more narrowly designed to this
> gaussian family model with an identity link?  If there's an argument
> in favor of PQL, I'd like to know it, because a couple of the
> Hypothesis tests based on t-statistics are affected.
> 
> 2. Is there a pre-made method for calculation of the robust standard
> errors?
> 
> I notice that model.matrix() command does not work for either lme or
> glmmPQL results, and so I start to wonder how people calculate
> sandwich estimators of the standard errors.
> 
> 3. Are the AIC (or BIC) statistics comparable across models?  Can one
> argue in favor of the glmmPQL results (with, say, a log link) if the
> AIC is more favorable than the AIC from an lme fit?  In JK Lindsey's
> Models for Repeated Measurements, the AIC is sometimes used to make
> model selections, but I don't know where the limits of this
> application might lie.
> 
> 
> -- 
> Paul E. Johnson                       email: pauljohn at ku.edu
> Dept. of Political Science            http://lark.cc.ku.edu/~pauljohn
> 1541 Lilac Lane, Rm 504 University of Kansas                  Office:
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> 864-5700
> 
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