Table of Contents

## What is overdispersion Poisson?

An assumption that must be fulfilled on Poisson distribution is the mean value of data equals to the variance value (or so- called equidispersion). If the variance value is greater than the mean value, it is called overdispersion.

**How does Poisson deal with overdispersion?**

Replace Poisson with Negative Binomial Another way to address the overdispersion in the model is to change our distributional assumption to the Negative binomial in which the variance is larger than the mean.

**How do you deal with Poisson and overdispersion regression?**

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 is overdispersion and Underdispersion?

Overdispersion means that the variance of the response is greater than what’s assumed by the model. Underdispersion is also theoretically possible but rare in practice. More often than not, if the model’s variance doesn’t match what’s observed in the response, it’s because the latter is greater.

**How much is too much overdispersion?**

Over dispersion can be detected by dividing the residual deviance by the degrees of freedom. If this quotient is much greater than one, the negative binomial distribution should be used. There is no hard cut off of “much larger than one”, but a rule of thumb is 1.10 or greater is considered large.

**What is overdispersion in ecology?**

overdispersion (contagious distribution) In plant ecology, a situation in which the pattern formed by the distribution of individuals of a given plant species within a community is not random but shows clumping, so that large numbers of both empty and heavily populated quadrats are recorded.

## How much overdispersion is too much?

**What is overdispersion in logistic regression?**

Overdispersion occurs when error (residuals) are more variable than expected from the theorized distribution. In case of logistic regression, the theorized error distribution is the binomial distribution. The variance of binomial distribution is a function of its mean (or the parameter p).

**How do you know you have overdispersion?**

### Is overdispersion a problem?

Overdispersion is a common problem in GL(M)Ms with fixed dispersion, such as Poisson or binomial GLMs. Here an explanation from the DHARMa vignette: GL(M)Ms often display over/underdispersion, which means that residual variance is larger/smaller than expected under the fitted model.

**How do you identify overdispersion?**

**How do you fix Overdispersion in logistic regression?**

A simple solution for overdispersion is to estimate an additional parameter indicating the amount of the oversidpersion. With glm(), this is done so-called ‘quasi’ families, i.e., in logistic regression we specify family=quasibinomial instead of binomial.