C. AMAL D. GLELE
altessedac2 at gmail.com
Tue Feb 6 23:13:30 CET 2018
Many thanks, Paul.
2018-02-06 15:53 GMT+01:00 Paul Johnson <paul.johnson at glasgow.ac.uk>:
> My preferred approach to overdispersion is none of 1-3 but to assume it
> applies (it usually does, for biological data anyway), and make sure the
> model includes a parameter to model overdispersion. For binomial (except
> Bernoulli) and Poisson you can include an observation-level random effect
> (OLRE). You can then gauge the amount of overdispersion in the model from
> the size of the OLRE variance estimate. (Note the OLRE will mop up
> variation due to *all* sources of lack of fit, including poor model
> specification, e.g. fitting a straight line where a curve would fit better.)
> Best wishes,
> > On 6 Feb 2018, at 14:29, C. AMAL D. GLELE <altessedac2 at gmail.com> wrote:
> > Hi, dear all
> > Please, your help for the following problem:
> > when I fit a mixed model using glmmTMB (poisson family or others), how
> do I:
> > 1) check, if the model fits well my data (goodness of fit)?
> > 2) check if my model is overdisperced or not (by using sigma(model)?)
> > 3) compute an pseudo R² to see the percentage of the variability of my
> > response which is explained by my model?
> > In advance, thanks for your answers.
> > Regards,
> > [[alternative HTML version deleted]]
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