generalized linear mixed models: large differences when using glmmPQL or lmer with laplace approximation

You make reference to my comment below, but I think you overstate my position a bit (the words in quotes are not a direct quote of what I said).

The original poster mentioned that 2 different methods gave 2 different models, one possibility is that one method gave a wrong model (biased in a non-good way), another possibility is that the predictor variables are correlated enough that there are multiple good models.  I merely pointed out that comparing the predicted values to the original values would be one way to possibly distinguish between the 2 cases.

Focusing too much on the predicted values can lead to overfitting, so we should not depend only on that.  P-values are useful in some cases, so I would not say "don't worry about the p-values" as a general statement.

The issue of editors wanting p-values even when they answer the wrong question is part of the result of statisticians doing to good a job of training other researchers.  Now it is our responsibility to continue to train them as to when to use certain tools.

--
Gregory (Greg) L. Snow Ph.D.
Statistical Data Center
Intermountain Healthcare
greg.snow at imail.org
801.408.8111

-----Original Message-----
From: r-sig-mixed-models-bounces at r-project.org [mailto:r-sig-mixed-
models-bounces at r-project.org] On Behalf Of Ben Bolker
Sent: Tuesday, October 07, 2008 11:44 AM
To: Martijn Vandegehuchte; R Mixed Models
Subject: Re: [R-sig-ME] generalized linear mixed models: large
differences when using glmmPQL or lmer with laplace approximation

Martijn Vandegehuchte wrote:

First of all, thanks a lot for the info.

I know the differences seem small, but most ecological journals still
let their opinion about ecological relevance of predictors depend
completely on p-values... So I think I'll stick to lmer because of

the

Laplace approximation.

  Well, Laplace should be better anyway.  (If the difference were in
the other direction -- non-significant with Laplace and significant
with
glmmPQL -- I would still tell you to use Laplace.)

  To speak to Greg Snow's comment ("don't worry about p-values, just
look at predictions") -- this is really tough.  I still don't know
what to do about the compromise between how statistics should be done
and how journal editors seem to insist it should be done ...

  cheers
   Ben

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