[R-sig-ME] ML or REML for LR tests
John Maindonald
john.maindonald at anu.edu.au
Wed Sep 10 08:49:35 CEST 2008
Douglas -
I am taking up your reply to the question that Austin
Frank asked on August 29. I'd like to check that my
understanding of the reply is correct.
Looking at page 59 of Pinheiro & Bates. \theta is a
parameterization of the ratio of the inverse of the variance-
covariance matrix of the random effects, multiplied by \sigma^2,
e.g., in a model with 3 levels of random effects,
\theta = (\sigma/\sigma_1, \sigma/\sigma_2) might do the job.
Both the profiled deviance and the profiled REML criterion
are evaluated at each step of the optimization, albeit
at the current REML estimate of \theta. (The ML deviance
can be determined without explicitly calculating the ML
estimates of the fixed and random effects.)
It makes little difference whether one evaluates the
likelihood deviance at the REML estimate of \theta or at
the ML estimate. More importantly for the anova table,
the difference in deviances is not much altered.
Thanks in anticipation
John Maindonald email: john.maindonald at anu.edu.au
phone : +61 2 (6125)3473 fax : +61 2(6125)5549
Centre for Mathematics & Its Applications, Room 1194,
John Dedman Mathematical Sciences Building (Building 27)
Australian National University, Canberra ACT 0200.
The reply was:
(From Doug Bates - 29 August 2008)
It may help to give a bit of background about what happens in the
anova method for mer objects in the lme4 package.
When fitting a linear mixed model, both the profiled deviance
(negative twice the log-likelihood) and the profiled REML criterion
are evaluated at each iteration during the optimization. The word
"profiled" means that, although the likelihood or the REML criterion
are defined as functions of the fixed-effects parameters,
$\boldmath{\beta}$, the common scale parameter, $\sigma$, and the
parameters $\boldmath{\theta}$ that determine the relative covariance
matrix $\boldmath{ \Sigma}$ of the random effects, these criteria are
evaluated as a function of $\boldmath{\theta}$ alone, with
$\boldmath{\beta}$ and $\sigma$ at their conditionally optimal values.
The profiled REML criterion has a term that depends on the model
matrix X for the fixed-effects parameters. If you change the
definition of the fixed-effects you will change the value of that
criterion in a systematic way that does not depend on how well the
respective models fit the observed data. Thus, differences in the
REML criterion are not a meaningful way to compare two models that
differ in the definition of the fixed-effects.
If you look at the code for the anova method for the mer class you
will see that it always calls logLik with REML = FALSE. That is, it
extracts the log-likelihood from the profiled deviance even if the
model has been fit according to the REML criterion. In general that
would be a very bad idea if we were using the deviance and not the
profiled deviance. You can't count on the deviance at the REML
estimates for the complete parameter vector being close to the optimal
deviance. However, the profiled deviance at the REML estimates of
$\boldmath{\theta}$ is close to the optimal profiled deviance.
For example,
fm1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy)
## profiled deviance at the REML estimate of $\theta$
dput(d1 <- deviance(fm1, REML = FALSE))
structure(1751.98581110077, .Names = "ML")
## optimal value of the deviance
dput(d2 <- deviance(update(fm1, REML = FALSE)))
structure(1751.93934480597, .Names = "ML")
## amount by which the profiled deviance at REML estimate
## exceeds the optimal value
d1 - d2
ML
0.04646629
As you can see, I have gravitated to using the term "REML criterion"
rather than calling it a deviance or a log-likelihood. The print
method for mer objects still labels it as the REMLdeviance but I plan
to change that and create an additional extractor called REML to
return that value. In that case the deviance extractor will always
return the deviance, perhaps after re-optimizing to produce the
optimal deviance.
So the bottom line is that the anova method for mer objects always
produces a likelihood ratio test based on the likelihood. If you
compare models fit by REML that likelihood ratio statistic will be
somewhat inaccurate but only by a small amount (in all the cases that
I have seen).
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