[R-sig-ME] ZINB model validation and interpretation
bbolker at gmail.com
Wed Sep 20 20:24:56 CEST 2017
This isn't actually a mixed-model question as far as I can tell, but
I'll take a stab at it. (https://stats.stackexchange.com is probably
the best option for follow-ups, as R-help isn't for general statistics
Your approach seems not-crazy to me, although I would probably be
lazier/slopper and compare all four cases (P, NB, ZIP, ZINB) in a
single AIC(c) table. In any case, there are very basic issues with
either P vs NB or ZIP vs ZINB tests based on any of the standard
approaches (Vuong, *IC, likelihood ratio test) that come from the fact
that one of the pair of models is on the boundary of the feasible
space, see e.g.
For validity and robustness, I would suggest more "impressionistic"
diagnostics (inspect residuals for independence of predictors, lack of
heteroscedasticity; look for influential/outlier residuals; compare
patterns of predictions with patterns in raw data for evidence of
unexpected patterns). If you want more formal tests, try generating
posterior predictive simulations of quantities that are important to
you and see if they match the observed values of those quantities.
On Mon, Sep 18, 2017 at 6:25 PM, <miriam.alzate at unavarra.es> wrote:
> I am working with a ZINB model in R. To validate it, I first did a VUONG
> test to compare it with a standard NB model. The result is that the ZINB
> is better than the NB. Then, I compared the ZINB to a ZIP model, comparing
> the AIC index and the log-likelihood and I also get that the ZINB fits
> better than the ZIP.
> However, I would like to know if I should take other tests into
> consideration to show the validity and robustness of my model.
> On the other hand, I would like to know if I can interpret the
> coefficients directly from the model result or I should compute the Odds
> Thanks a lot,
> R-sig-mixed-models at r-project.org mailing list
More information about the R-sig-mixed-models