[R-sig-ME] lmer vs glmmPQL

Fabian Scheipl Fabian.Scheipl at stat.uni-muenchen.de
Tue Jun 30 18:24:33 CEST 2009

On Tue, Jun 30, 2009 at 9:16 AM, Ken Beath<ken at kjbeath.com.au> wrote:
> It appears that PQL with moderate random effect variance introduces a small
> bias in a direction that reduces the MSE, at least in the simulations
> chosen. For large variances the bias is probably excessive and the MSE will
> increase using PQL.

Results from simulations with sd(RandomIntercept)=3 instead of 1
(results attached) confirm your remark - with the possible exception
of very small data sets the performance (in rmse & bias) for Laplace
and AGQ is much much better than PQL.
I'm sorry for getting Ben Bolker and others all riled up with my earlier post.

One more thing to consider though:
 A random intercept variance of 1 in a logistic model means that the
medium  50% of subjects/groups are expected to have between about half
and about double the odds of a subject/group with random intercept=0,
which is already fairly large effect in my book.
> qlnorm(c(.1, .25, .75, .9))
[1] 0.28 0.51 1.96 3.60

For a random intercept sd of 3, the multiplicative effect on the
baseline odds for the middle 50% is between  0.13 and  7.6,
>qlnorm(c(.1, .25,  .75, .9), sdlog = 3)
[1]  0.021  0.132  7.565 46.743
which means really large inter-group/subject heterogeneity and might
not be encountered that frequently in real data (?) (or at least
suggest a mis-specified model that misses important
subject/group-level predictors...).

(Similar remarks concerning "effect size" of the random effect apply
to Poisson regression with log-link.)

So, what's the lesson --
Should we still prefer PQL if we expect to see small to intermediate
inter-group/subject heterogeneity?

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