Is there an overdispersion problem in a quasi-binomial GLM?

Is there an overdispersion problem in a quasi-binomial GLM?

Overdispersion problem in a quasi-binomial GLM (for proportional data)? Below is the summary of a GLM I built (using R) for a response variable which is proportional (derived from count data). My only predictor is a continuous one (environmental measurement).

Why do we need a quasibinomial model in GLM?

Typically data are either under-dispersed or over-dispersed, which means that the variance (as a function of E(yj) E ( y j)) is too big or too small, and so does not match what we see in the field. A Quasibinomial model is a possible remedy in this situation.

How does quasibinomial differ from the binomial distribution?

How quasibinomial differs to the binomial distribution. When the response variable is a proportion (example values include 0.23, 0.11, 0.78, 0.98), a quasibinomial model will run in R but a binomial model will not.

How are confidence intervals determined in a quasi likelihood model?

The Quasibinomial model adds an extra dispersion parameter to the variance, so it has slightly different central moments. Quasi likelihood models do not specify data generating processes on the data. Rather, just the mean and variance are specified, which is enough to determine confidence intervals for parameters.

Is there such a thing as overdispersion in GLM?

Overdispersion occurs because the mean and variance components of a GLM are related and depends on the same parameter that is being predicted through the independent vector. There is no such thing as overdispersion in ordinary linear regression. In a linear regression model

Can you use a quasi binomial error distribution?

I used a “successes and failures” kind of approach so that I can use binomial (and quasi-binomial) error distributions. It’s also possible to model proportional data that way. But I think we are both missing something here.

Why does overdispersion occur in the generalized linear model?

The greater variability than predicted by the generalized linear model random component reflects overdispersion. Overdispersion occurs because the mean and variance components of a GLM are related and depends on the same parameter that is being predicted through the independent vector.

Which is a better fit for beta binomial GLM?

I think beta-binomial (also available from “gamlss” package) is a better fit. I started with binomial actually. My data are proportions (percentages actually – percent representation of a certain functional groups in the community) derived from annual counts, so yes, I only have a single data point for each year.

Can a quasipoisson family be used in a glmer function?

However, overdispersion was detected and the family “poisson” therefore cannot be used. Moreover, “quasipoisson” families are not supported by the glmer function. Can anyone suggest a solution for this problem?