[R-sig-ME] Help with glmmTMB mixed models
bbolker @ending from gm@il@com
Tue Jun 5 15:56:41 CEST 2018
I think I'd recommend an ordinal response (e.g. using the clmm function
from the ordinal package). Other than being a positive integer-valued
values, I don't think group size really matches the mechanism of a
truncated Poisson very well.
I'm not sure how to test goodness-of-fit for CLMM. If you use clmm2
instead of clmm, you'll be able to get predicted values from the model,
which you examine to get an intuitive idea of how well the model is fitting.
On 2018-06-04 08:42 PM, Harish Tiwari wrote:
> I am working on control of dog-bite related rabies in India. In this
> regard, I want to construct a mixed model that could predict the
> grouping behaviour of free-roaming dogs as solitary ( found singly),
> in pairs, in triads or in the groups of four or more dogs. As
> basically it is a count data, and chances of sighting no dog are not
> accounted, this response variable follows a zero-truncated Poisson
> distribution. The predictors are a mix of numerical ( resight
> probability, temperature, humidity and wind velocity of the day of
> the survey) and categorical ( gender, age, body condition score and
> if sighted in the proximity of garbage dumps) variables.
> The data was collected by the survey of free-roaming dogs over 7
> survey occasions in the manner of capture-recapture data ( only here
> it was sight-resight). As many individuals were sighted more than
> once during the survey, and their measures are repeated, mixed models
> with random effect were thought to be the way to account for the
> clustering. I modelled the data on the glmmTMB package (the
> intercepts, however, did not differ much when the model was
> constructed using VGAM -vglm function). I seek to resolve some
> queries I have in this regard:
> 1. Is the glmmTMB package appropriate to model this kind of data? 2.
> How to test goodness of fit of the model?
> Help is greatly appreciated
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