ZINB model validation and interpretation
Please keep r-sig-mixed-models in the Cc: (this is borderline since the questions are a bit off topic, but the more important [to me] point is that I don't want to engage in off-list conversations about stats help outside of direct collaborations ...)
On Fri, Oct 6, 2017 at 4:11 PM, <miriam.alzate at unavarra.es> wrote:
Hi Ben, Many thanks for the answer and sorry for the delay. The first part is ok. Would you compute the BIC test as well? The point is that I am getting a N/A when I run the BIC test in R for my ZINB and ZIP models, it works right for the P and NB. AIC, VUONG and likelihood ratio test are Ok.
BIC, AIC, likelihood, and Vuong are all reasonable approaches to model testing. They all answer slightly different questions, and you should be aware of the differences, e.g. see http://emdbolker.wikidot.com/blog:aic-vs-bic . I don't know why you get NA values; a reproducible example would be helpful (you should probably post it to the glmmTMB issues list <https://github.com/glmmTMB/glmmTMB/issues>
The second part is quite unfamiliar for me. Could you let me know the package or what kind of code should I use for it? I have read something about bootstrapping.
You can use the simulate() function for a sort of posterior predictive simulation (although not one that takes uncertainty in the parameter estimates into account ...) see e.g. Gelman and Hill 2007.
Thanks a lot Miriam El Mie, 20 de Septiembre de 2017, 20:24, Ben Bolker escribi?:
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 questions.) 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. https://stats.stackexchange.com/questions/182020/zero-inflated-poisson-regression-vuong-test-raw-aic-or-bic-corrected-results/217869 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:
Hello, 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 ratios. Thanks a lot, Miriam
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