[R-sig-ME] valid estimates using lme4?

[Dimitris Rizopoulos]

I think the reviewer refers to the fact that the Laplace approximation 
is equivalent to using the *adaptive* Gauss-Hermite rule with one 
quadrature point.

I feel that he/she is right in pointing out that the Laplace 
approximation may not always work satisfactorily for binary data (for 
binomial data with more than one trials it would be a bit better), and 
it would be therefore prudent to validate your results by fitting the 
models with the adaptive Gauss-Hermite rule but with more than one 
quadrature points. For this check argument 'nAGQ' of glmer().


I hope it helps.

Best,
Dimitris


On 10/28/2011 4:04 PM, Vernooij, J.C.M. (Hans) wrote:
> Dear list members,
>
> For a concept article we used package lme4 for a logistic regression. A reviewer doubts about the validity of the outcomes:
> "I strongly urge you to compare the outcomes of lme4 in R with a validated statistical package (SAS, STATA, SPSS) as lme4 is known not to be the best, especially when the Laplace approximation is being used as the default is only one (!) integration point". (quoted)
>
> How to repond to this? In http://glmm.wikidot.com/faq the Laplace estimation is said to be less accurate than Gaus-Hermite quadrature or MCMC methods but is the difference in estimates such that the results are not valid? Should we validate the results by running different packages ? Undoubtly we will find differences so what results to report?
> What answer might convince the reviewer?
>
> Thanks,
> Hans
>
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-- 
Dimitris Rizopoulos
Assistant Professor
Department of Biostatistics
Erasmus University Medical Center

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