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

[Baldwin, Jim -FS]

I'd like to suggest that the phrase "we can't discuss main effects in the presence of a statistically significant interaction" isn't so cut-and-dry.  It depends.

If the size of the main effects is far greater than additional interaction effect, then one can certainly talk about main effects.  The catch is knowing about "practical" or "subject matter" significance as it is not solely a statistical issue.

It is the "interpretation" of results that can be problematic and not necessarily the fault of SAS or R or any other software package that provides the results.

Jim


-----Original Message-----
From: R-sig-mixed-models [mailto:r-sig-mixed-models-bounces at r-project.org] On Behalf Of Ken Beath
Sent: Wednesday, March 04, 2015 2:57 PM
To: Megan Kutzer
Cc: r-sig-mixed-models at r-project.org
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/

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