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
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