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Here we have clearly overdispersion: library(AER) deviance(mod1)/mod1$df.residual dispersiontest(mod1) Check for influential and leverage points, e.g., with the influencePlot in the car package. … Here a zero inflated model would fit better than the simple Poisson (again probably due to overdispersion): library(pscl) mod2 <- zeroinfl(Days~Age+Sex, data=quine, dist="poisson") AIC(mod1, mod2) Plot …

the expected amount of variance, whereas $\phi<1$ means that we have less than the expected variance (called underdispersion) and $\phi>1$ means that we have extra variance beyond the expected (called overdispersion …

In the R package AER you will find the function dispersiontest, which implements a Test for Overdispersion by Cameron & Trivedi (1990). … The test simply tests this assumption as a null hypothesis against an alternative where $Var(Y)=\mu + c * f(\mu)$ where the constant $c < 0$ means underdispersion and $c > 0$ means overdispersion. …

I'm hoping someone can provide an intuitive overview of what quasibinomial distribution is and what it does. I'm particularly interested in these points: How quasibinomial differs to the binomial di …

To check for overdispersion I'm looking at the ratio of residual deviance to degrees of freedom provided by summary(model.name). …

This situation is called overdispersion and negative binomial regression is more flexible in that regard than Poisson regression (you could still use Poisson regression in that case but the standard errors …

If e is an unknown noise component, then there is some unobserved heterogeneity causing a little bit of overdispersion which could be captured by a negative binomial model or some other kind of continuous …

Yes, it can deal with overdispersion. But take care not to confuse the conditional dispersion with the unconditional dispersion. …

You should be careful to check for/account for overdispersion. … “A Comparison of Observation-Level Random Effect and Beta-Binomial Models for Modelling Overdispersion in Binomial Data in Ecology and Evolution.” …

I want to fit a multilevel GLMM with a Poisson distribution (with over-dispersion) using R. At the moment I am using lme4 but I noticed that recently the quasipoisson family was removed. I've seen e …

Page 129, Box 1: The residuals indicated overdispersion, so we refitted the data with a quasi-Poisson model. … Residuals plots should be used to assess overdispersion and transformed variances should be homogeneous across categories. …

The standard errors can be computed in various ways: (a) scaled standard errors via the overdispersion estimate (one, two). This is called "quasi-binomial" GLM. … Using logit transform of the response: lmer(log(p/(1-p)) ~ a+b+c + (1|subject), myData) Accounting for overdispersion in the binomial model. …

frac{\theta+2\mu}{\sqrt{\theta\mu(\theta+\mu)}} = \frac{1 + 2\frac{\mu}{\theta}}{\sqrt{\mu(1+\frac{\mu}{\theta})}} $$ In this context, $\theta$ is usually interpreted as a measure of overdispersion …

Poisson regression is just a GLM: People often speak of the parametric rationale for applying Poisson regression. In fact, Poisson regression is just a GLM. That means Poisson regression is justified …

Quasi-likelihood is one way of handling overdispersion; if you don't address overdispersion in some way, your coefficients will be reasonable but your inference (CIs, $p$-values, etc.) will be garbage. … As you comment above, there are lots of different approaches to overdispersion (Tweedie, different negative binomial parameterizations, quasi-likelihood, zero-inflation/alteration). …