[R] best subset selection on random effects model

[ilai]

I don't know of any package that will do it (or if violating the
marginality principle by having non-nested models even makes sense)
but you could always build your own search through all possible
models. Problem is for a large number of fixed effects this can take a
good bit of time...
Here is a piece of code I had from a few of years back. There may be
more elegant solutions that will reduce run time such as using
update.lme. Basically my code finds all possible combinations of model
terms with ?combn and than fits them.

require(nlme)
fm <- lme(distance ~age*Sex, random = ~ 1|Subject,data=Orthodont,method='ML')
Terms <- terms(fm)
todrop <-  1:length(attr(Terms,'term.labels'))
subs <- unlist(sapply(todrop,function(p)
combn(todrop,p,simplify=F)),recursive =F)
fm.subList <- lapply(subs[-length(subs)],function(s,...){
  newf<- formula(drop.terms(terms(fm),s,keep.response = TRUE))
  update(fm,newf)
})
names(fm.subList) <- sapply(fm.subList, function(x) paste('fm',attr(
terms(x),'term.labels'),sep='.'))
sort(sapply(fm.subList,BIC))               # 1st is best based on BIC

Cheers


On Tue, Feb 14, 2012 at 11:25 AM, Tao Zhang <zt020200 at gmail.com> wrote:
> The models are not nested. I would like to consider all the possible
> subsets.
> I hope to output a table, where each row of the table indicates a best
> subset of the fixed effects for a particular model size.
>
> Thank you,
> Tao
>
>
> 2012/2/13 ilai <keren at math.montana.edu>
>>
>> The question is where do your models come from? Passing nested models
>> to ?anova.lme in nlme package or lme4 results in a likelihood ratio
>> test. Are you looking for something else/more ?
>>
>>
>> On Sun, Feb 12, 2012 at 8:02 PM, Tao Zhang <zt020200 at gmail.com> wrote:
>> > Hi,
>> >     I know leaps() computes the best subset selection for linear model,
>> > and the bestglm() computes the best subset selection for generalized
>> > linear
>> > model.
>> > Is there any package for best subset selection on random effects model,
>> > or
>> > mixed effects model?
>> >
>> > Thank you so much.
>> >
>> > Tao
>> >
>> >        [[alternative HTML version deleted]]
>> >
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>
>