[R-sig-ME] extracting p values for main effects of binomial glmm

[Steve Denham]

Hmm.  I have never had a problem interpreting interactions that I got from SAS procedures (MIXED, GLIMMIX, HPMIXED).  What do you mean as 'not sensible'?
Thanks, Steve Denham
Director, Biostatistics
MPI Research, Inc.
 
      From: Ken Beath <ken.beath at mq.edu.au>
 To: Megan Kutzer <makutzer at gmail.com> 
Cc: "r-sig-mixed-models at r-project.org" <r-sig-mixed-models at r-project.org> 
 Sent: Wednesday, March 4, 2015 5:56 PM
 Subject: Re: [R-sig-ME] extracting p values for main effects of binomial glmm
   
That is what I though you meant. In that case you can't discuss main
effects at all, as the effect of diet, for example, is different for each
combination of infection status and day. SAS and some other software will
attempt to give results but they aren't usually sensible.

On 5 March 2015 at 09:44, Megan Kutzer <makutzer at gmail.com> wrote:

> No, sorry, the model is Diet + infection status + day and all the two way
> interactions and the 3 way interaction.
> On 4 Mar 2015 23:34, "Ken Beath" <ken.beath at mq.edu.au> wrote:
>
>> Did yo mean to have interactions between all 3 as "Diet * Infection
>> status * Day". With interactions it isn't possible to test for the effect
>> of main effects.
>>
>> On 5 March 2015 at 07:11, Megan Kutzer <makutzer at gmail.com> wrote:
>>
>>> Hi,
>>>
>>> I'm fairly new to mixed models and have done a lot of reading without
>>> much
>>> success. Unfortunately there is no one at my institution who is really
>>> familiar with them so I thought I would try this list.
>>>
>>> I'm running a binomial generalized linear mixed effects model and I need
>>> p-values for the main effects. I know this isn't entirely correct with
>>> this
>>> type of model but my supervisor wants the p-values!
>>>
>>> The model is:
>>>
>>> glmer (Proportion hatched ~ Diet * Infection status * Day +
>>> (1|SubjectID) +
>>> (1|Day), family=binomial)
>>>
>>> where,
>>>
>>> Proportion hatched = cbind(Offspring, Eggs-Offspring)
>>> Diet is a factor with 2 levels
>>> Infection status is a factor with 4 levels
>>> Day is a factor with 3 levels
>>>
>>> Using Subject ID number and Day as random effects is supposed to control
>>> for pseudoreplication in the model, although I am not entirely sure that
>>> this is specified in the correct way. I wanted to include experimental
>>> replicate here too but the model failed to converge.
>>>
>>> My question is: is there a way to get p-values for the main fixed effects
>>> of Diet, Infection and Day?
>>>
>>> If you need more specific model information or the model output I would
>>> be
>>> happy to provide it.
>>>
>>> Thanks,
>>> Megan
>>>
>>>        [[alternative HTML version deleted]]
>>>
>>> _______________________________________________
>>> R-sig-mixed-models at r-project.org mailing list
>>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>>
>>
>>
>>
>> --
>>
>> *Ken Beath*
>> Lecturer
>> Statistics Department
>> MACQUARIE UNIVERSITY NSW 2109, Australia
>>
>> Phone: +61 (0)2 9850 8516
>>
>> Building E4A, room 526
>> http://stat.mq.edu.au/our_staff/staff_-_alphabetical/staff/beath,_ken/
>>
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>>
>>


-- 

*Ken Beath*
Lecturer
Statistics Department
MACQUARIE UNIVERSITY NSW 2109, Australia

Phone: +61 (0)2 9850 8516

Building E4A, room 526
http://stat.mq.edu.au/our_staff/staff_-_alphabetical/staff/beath,_ken/

CRICOS Provider No 00002J
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