How does Poisson regression fix overdispersion?
How to deal with overdispersion in Poisson regression: quasi-likelihood, negative binomial GLM, or subject-level random effect?
- Use a quasi model;
- Use negative binomial GLM;
- Use a mixed model with a subject-level random effect.
What other model can be used to account for overdispersion?
Poisson regression mixed-effect model
The only model able to take into account overdispersion was the Poisson regression mixed-effect model with nested random effects at the site and date of capture levels.
What to do when there is overdispersion in a Poisson model?
Quasi-poisson is one possibility when there is overdispersion. Others include: Negative binomial regression (NBR) – similar to Poisson model, but using the negative binomial distribution instead, which has a dispersion parameter. Available in the MASS package in R, also integrated into Stata.
When to use a truncated Poisson / NB model?
Hurdle regression – for circumstances with more 0s than would be expected from the Poisson/NB model. It combines a logit/probit with Poisson/NB, where the logit/probit is used to estimate y=0 vs y>0, and a truncated Poisson/NB is used to estimate the cases where y>0. Available in the pscl package.
When to use hurdle regression with Poisson / NB?
Available in the MASS package in R, also integrated into Stata. Hurdle regression – for circumstances with more 0s than would be expected from the Poisson/NB model. It combines a logit/probit with Poisson/NB, where the logit/probit is used to estimate y=0 vs y>0, and a truncated Poisson/NB is used to estimate the cases where y>0.
When do we use a Poisson regression model?
T he Poisson regression model naturally arises when we want to model the average number of occurrences per unit of time or space. For example, the incidence of rare cancer, the number of car crossing at the crossroad, or the number of earthquakes. One feature of the Poisson distribution is that the mean equals the variance.