factor elimination for lmer models
On 3/20/07, Austin Frank <austin.frank at gmail.com> wrote:
I'll preface this by saying that this question may reveal some fundamental misunderstanding on my part. Thanks in advance for clarification if there are things that I'm obviously not getting. That said...
The Design library provides a function fastbw that does fast backwards elimination of factors in a model. This is a very useful function, and I'm wondering what it would take to make it work with lmer or lmer2-fitted models.
The function works on any model, m, where Varcov(m) is defined. Varcov is a function from Hmisc that extracts the variance-covariance matrix from certain kinds of fitted models. Currently it works for lm, glm, and multinom fitted models.
So, five questions:
1) Is there already a way to do automated factor elimination on an lmer-fitted model?
Not that I know of.
2) Would it be possible to write a function Varcov.lmer to extract the variance-covariance matrix from an lmer-fitted model?
I didn't go through the documentation for Varcov, which is part of a large file related to transcan, in detail but it appears on simple examples that Varcov produces the same result as vcov and there is a vcov method for the lmer class.
3) Would it be possible to port the fastbw function from the Design library so that it would work on lmer models (without relying on Varcov from Hmisc)?
It depends on what fastbw does. That name seems to imply that it will create the results from refitting a model omitting certain terms in the model specification and it will do so without needing to refit. I don't think that would be possible for lmer models because when you omit a term in the fixed-effects specification you will change the estimates of the variance components. The results from the vcov method are conditional on the values of the variance components.
4) If both 2 and 3 are possible, which is the path of least resistance? 5) If neither 2 nor 3 is possible (the algorithm used in Design's fastbw is unsuitable for lmer models), is there an approach to factor elimination that is appropriate for these models? If you folks get me started in the right direction, I'd be happy to submit a patch to lme4 or languageR.