Thanks for the question. In this particular case which is atheoretical, I showed poisson, negative binomial, and their zero-inflated counterparts. As you note, negative binomial is typically the route for accounting for overdispersion, but what appears as overdispersion can be due to zero-inflation. And notably, I showed a zero-inflated negative binomial model, which could theoretically account for a case where there is both zero-inflation and overdispersion. Hope that makes sense!
I think I misunderstood. You’re saying use quasi-poisson to address over dispersion? Which type of model are you referring to? Quasi-poisson is often used for under dispersion.
@@WestDesignAnalysis your first answer clears it up, thanks for the response. do you have any videos/articles for obtaining probabilities using GLMs/Mixed Models? Cheers
For generalized linear models you can use the predict function in R. I show that in the “intro to GLM” video. If you’re asking about predicted probabilities in generalized linear mixed effect models, that is a bit more complicated.
Why did you not use quasi-poisson to account for the over dispersion rather than a negative binomial model? Or is there not much difference?
Thanks for the question. In this particular case which is atheoretical, I showed poisson, negative binomial, and their zero-inflated counterparts. As you note, negative binomial is typically the route for accounting for overdispersion, but what appears as overdispersion can be due to zero-inflation. And notably, I showed a zero-inflated negative binomial model, which could theoretically account for a case where there is both zero-inflation and overdispersion. Hope that makes sense!
I think I misunderstood. You’re saying use quasi-poisson to address over dispersion? Which type of model are you referring to? Quasi-poisson is often used for under dispersion.
@@WestDesignAnalysis your first answer clears it up, thanks for the response.
do you have any videos/articles for obtaining probabilities using GLMs/Mixed Models? Cheers
For generalized linear models you can use the predict function in R. I show that in the “intro to GLM” video. If you’re asking about predicted probabilities in generalized linear mixed effect models, that is a bit more complicated.