ZINB or ZIP for cetacean data with many zeros

Question

I am stuck in my thesis to conduct a GLM with cetacean data (count, density, effort and covariates). I have over 94% zeros in my dataset meaning I should be using a GLM with zero-inflated negative binomial/ or Poisson? Do I still need an offset within the equation? In other journal papers regarding cetaceans I have never read about ZINB or ZIP. Would it not be very common?

Thanks for your help.

Just because there are many zeros does not necessarily mean you need a zero inflated model; a Poisson or negative binomial regression may be adequate. I would recommend you follow the model selection approach gung proposes in the proposed duplicate. — Stephan Kolassa, Jun 30 '21 at 10:05
But it looks like this Q has a better answer than the proposed dup ... — kjetil b halvorsen, Jul 01 '21 at 22:45

score 1 · Answer 1 · answered Jun 30 '21 at 22:14

Indeed having 94% of zeros sounds like a rather large proportion of zeros so your original idea of using a zero-inflated or a hurdle model is not unfounded. Any reasonable analyst would. :)

That said, as Stephan mentioned in his comments, the large proportion of zeros does not necessitate the need for a zero-inflated or a hurdle model. I think it is very likely that you will indeed need a ZI count model (94% seems very large without any context). I would suggest looking at some formal references for example: Hilbe's Modeling Count Data, Chapt. 7 "Problems with Zeros", is very nice and accessible. It mentions a number of approaches (e.g. Boundary likelihood ratio tests, Vuong tests, etc.) Zuur et al. Mixed Effects Models and Extensions in Ecology with R Chapt. 11 "Zero-Truncated and Zero-Inflated Models for Count Data" is also consider quite a standard reference.

Regarding the use of an offset: Using an offset is mostly relevant if it makes sense to view the response variable as part of a rate instead of raw counts (e.g. number of infected individuals per 100K). Without knowing your exact research question one cannot answer this definitively; the interpretation of offset has been covered a couple of times in this forum, eg. see the following threads for more info:

In any case, it would be good to consider using rootgrams to visualise your results. Kleiber & Zeileis (2014) Visualizing Count Data Regressions Using Rootograms is a good reference for the matter (free version here).

A final comment about the perceived lack of ZI model use in "journal papers regarding cetaceans": It might be the case that in the papers you have seen ZI/hurdle models were unnecessary, unable to be estimated correctly or were simply ignored; we don't know that. Do not hesitate using a "more sophisticated" model; ultimately it is a matter if such a model (hierarchical, spatial, zero-inflated, what have you) is relevant to our research question and if we can correctly estimate it.

Wow, thank you for the long and in-depth answer. I will follow your advice and get back to you if things remain unclear. — Leonie Lepple, Jul 03 '21 at 15:45
I am glad I could help. If this answer is helpful please consider upvoting it and if it resolves your question marking it as the accepted answer. — usεr11852, Jul 03 '21 at 18:07
I realised that my GLM model is underdispersed: M4<-glm(HP_CT~BAT+FEA+AnTPV+HU3,family=poisson(), data=irishseafront) where the dispersion factor was =0.955. calculated by `dispersion <- M4$deviance / M4$df.residual’ . Is this close enough to 1 in order to proceed with a Poisson, or maybe even ZIP? Or do I need to address the minor under-dispersion - if so, how? Thanks again for any ideas or comments — Leonie Lepple, Jul 05 '21 at 17:02
This is very likely OK. You can try a hurdle model as a simple solution or a generalised Poisson. There are some even more specialised models (e.g. Conway-Maxwell-Poisson but for that you describe they seem like an overkill. It is important to note that it will be good to examine as why you think under-dispersion occurs (if indeed it occurs). — usεr11852, Jul 05 '21 at 19:39

ZINB or ZIP for cetacean data with many zeros

1 Answers1