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

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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).

  Fair enough.  Sorry about that.
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.

  Looking at the parameters, they seemed to be pretty similar to me,
although of course the details of the data (range of predictor
variables) matters too.
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.

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

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