Timeline for Likelihood ratio test for poisson vs negative binomial GLM
Current License: CC BY-SA 4.0
11 events
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| Dec 20, 2019 at 10:27 | comment | added | Tobit | Thanks for the useful link, do you know of any threads where such rootograms are fitted? I have included the pearson residual vs fitted plots for both the negative binomial and poisson model, neither looks great... I'll check out the DHARMa package, thanks! | |
| Dec 18, 2019 at 14:38 | history | edited | Stefan | CC BY-SA 4.0 |
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| Dec 18, 2019 at 14:32 | history | edited | Stefan | CC BY-SA 4.0 |
added 180 characters in body
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| Dec 18, 2019 at 14:15 | comment | added | Stefan |
General residual checks can be done in this way: plot(resid(mymodelfit, type = "pearson") ~ fitted(mymodelfit). Another option for checking residuals would be the DHARMa package: cran.r-project.org/web/packages/DHARMa/vignettes/DHARMa.html
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| Dec 18, 2019 at 14:15 | comment | added | Stefan | What you need to figure out is whether the negative binomial model accounts for the extra zeros (i.e. zeros that cannot be accounted for by using a Poisson model) - and it might, judging by the dispersion coefficient. What I was referring to and what I thought was explained in the paper are rootograms. But those are explained in a separate paper: arxiv.org/pdf/1605.01311.pdf | |
| Dec 18, 2019 at 11:00 | history | migrated | from stackoverflow.com (revisions) | ||
| Dec 18, 2019 at 10:21 | comment | added | Tobit | Sadly I can't share the data, what aspect would you need to see? Yes I have a lot of zeros, however these are all 'true' zeros, meaning that zero-inflated models that assume different causes of zeros and positive integers are, to my knowledge, inappropriate. I could not find any diagnostics in the pscl package? I found the odTest function that essentially tests whether a NB is better than a poisson model on the grounds of dispersion (could just compare dispersion coefficients instead?) | |
| Dec 18, 2019 at 9:39 | vote | accept | Tobit | ||
| Dec 16, 2019 at 20:40 | comment | added | Stefan | It seems like that the negative binomial model is more appropriate - but it's very difficult to judge without seeing the data. I am guessing you have too many zeros? Another option you might want to look at are zero-inflated and hurdle models. See here: cran.r-project.org/web/packages/pscl/vignettes/countreg.pdf The pscl package also contains diagnostics to check for model fit etc. Good luck! P.S. If you found this answer useful please consider accepting it. | |
| Dec 16, 2019 at 11:31 | comment | added | Tobit | Thank you for your response Stefan! I have analysed the dispersion coefficient of both models and found that of the negative binomial model to be 0.83 or 1.34, while that of the poisson model is 16.37 or 15.81 (via two different methods). Further, the AIC of the two models is 740 and 316 for the poisson and negative binomial model, respectively. I wasn't sure which residuals to check (and against what), but I'll check out the ones in the link you attached, though are these checks only relevant to poisson GLMs or also negative binomial GLMs? | |
| Dec 13, 2019 at 13:23 | history | answered | Stefan | CC BY-SA 4.0 |