[R-sig-ME] understanding log-likelihood/model fit
Andrew Robinson
A.Robinson at ms.unimelb.edu.au
Wed Aug 20 21:24:52 CEST 2008
I'll take a swing at these.
On Wed, Aug 20, 2008 at 02:01:29PM +0100, Daniel Ezra Johnson wrote:
> Everyone agrees about what happens here:
>
> Nsubj <- 10
> Ngrp <- 2
> NsubjRep <- 5
> set.seed(123)
> test1s <- data.frame(subject = rep(seq(Nsubj * Ngrp), each = NsubjRep),
> response=500+c(rep(-100,Nsubj * NsubjRep),rep(100,Nsubj *
> NsubjRep))+rnorm(Nsubj * Ngrp * NsubjRep, 0, 10),
> fixed=(rep(c("A","B"),each=Nsubj * NsubjRep)))
> null1 <- lmer(response~(1|subject),test1s)
> fixed1 <- lmer(response~fixed+(1|subject),test1s)
>
> I still have two questions which I'll try to restate. I should note
> that I have attempted to understand the mathematical details of ML
> mixed effect model fitting and it's a bit beyond me. But I hope that
> someone can provide an answer I can understand.
>
> Question 1: When you have an "outer" fixed effect and a "subject"
> random effect in the same model, specifically why does the model
> (apparently) converge in such a way that the fixed effect is maximized
> and the random effect minimized? (Not so much why should it, as why
> does it? This is the 'fixed1' case.)
A common way for hierarchical models to be fit using ML is by
profiling out the fixed effects, estimating the random effects, and
then using GLS to estimate the fixed effects conditional on the random
effects. So, any explanatory capacity that the fixed effects offer is
deployed before the random effects are invoked.
Likewise a popular way to applying ReML is to fit the fixed effects
using OLS, then estimate the random effects from the residuals.
Again, the net effect is that any explanatory capacity that the fixed
effects offer is deployed before the random effects are invoked.
> Question 2: Take the fixed1 model from Question 1 and compare it to
> the null1 model, which has a random subject effect but no fixed
> effect. The predicted values of the two models -- the ones from
> fitted(), which include the ranefs -- are virtually the same. So why
> does fixed1 have a lower deviance, why is it preferred to null1 in a
> likelihood ratio test? (Again, I'm not asking why it's a better model.
> I'm asking questions about the software, estimation procedure, and/or
> theory of likelihood applied to such cases.)
The deviance is computed from the log likelihood of the data,
conditional on the model. The LL of the null model is maximized by
making the variance components big enough to cover the variation of
the data. But, this means that the likelihood is being spread thinly,
as it were. Eg ...
> dnorm(0, sd=1)
[1] 0.3989423
> dnorm(0, sd=2)
[1] 0.1994711
On the other hand, fixed1 uses fixed effects to explain a lot of that
variation, so that when the time comes to estimate the random effects,
they are smaller, and the LL is higher, because it doesn't have to
stretch so far.
Cheers,
Andrew
--
Andrew Robinson
Department of Mathematics and Statistics Tel: +61-3-8344-6410
University of Melbourne, VIC 3010 Australia Fax: +61-3-8344-4599
http://www.ms.unimelb.edu.au/~andrewpr
http://blogs.mbs.edu/fishing-in-the-bay/
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