[R-sig-ME] generalized linear mixed models: large differences when using glmmPQL or lmer with laplace approximation

[Ben Bolker]

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Greg Snow wrote:
> 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
>>
>> _______________________________________________
>> R-sig-mixed-models at r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models

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