I have a small dataset of count data that has a number of zero measurements. My overdispersion is fairly large. Due to this, I was drawn to using the glmmadmb package.
I understand how to compare the fit of the distributions (poisson, nbinom etc) using AICtab.
What I am struggling with is how to assess whether the model with the 'best' fit of distribution actually fits my data. On 'normal' glm's, this is where you would use
I am aware, however, that the assumptions required to use this are not met in negative binomials etc etc.
Therefore, I am wondering if someone would point me in the right direction of finding an answer.... how would you validate your model choice in GLMMADMB?
Many thanks in advance for your help, I really appreciate your time and efforts.
EDIT: 8th May 2014
My data set is a very very simple one….. I am testing a behaviour response to an audio playback stimuli. I have 10 animals that are played a control playback, and the same 10 animals played a treatment playback the following day (alternated to balance treatment order effects). Therefore, I have only one fixed effect (Audio Treatment) and one random effect (Individual Name). I feel that I am comfortable with
lmer functions and have used
lme4 on several, much larger models, of both count and binomial. I am a little unsure of my understanding of
glmmadmb models…. What I see the issue as being with this small model, is that 5 / 10 of the measured responses for control playback are 0 (with the other 5 / 10 being small numbers, and 10 / 10 to treatment playback being large numbers).
My dataset looks as follows:
Individual Audio_Treatment Behaviour A TREATMENT 60 B TREATMENT 29 C TREATMENT 32 D TREATMENT 40 E TREATMENT 3 F TREATMENT 25 G TREATMENT 21 H TREATMENT 50 I TREATMENT 11 J TREATMENT 43 A CONTROL 4 B CONTROL 0 C CONTROL 0 D CONTROL 0 E CONTROL 19 F CONTROL 10 G CONTROL 4 H CONTROL 0 I CONTROL 0 J CONTROL 3
And I have fitted the following model:
modelNBIT<-glmmadmb(BEHAVIOUR~AUDIO_TREATMENT+(1|INDIVIDUAL), family="nbinom1", zeroInflation = TRUE)
I had initially thought that truncated models (or hurdle models) might be more suited to this, however the truncated glmmadmb runs the following error....(perhaps due to overfitting)
Parameters were estimated, but not standard errors were not: the most likely problem is that the curvature at MLE was zero or negative
Going back to my original question – I was wondering if someone would be able to suggest a way to validate the model (in this precise case, modelNBIT)?