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The Poisson distribution has a variance equal to its mean, $$\newcommand{\Var}{\operatorname{Var}} \Var(y) = E(y) = \lambda $$ The negative binomial model relaxes this assumption by estimating an overdispersion … Model logLik alpha p(alpha) Poisson -7942 1 NA NB1 -6399 1.001 0 NB2 -7944 403.4 0 Clearly the overdispersion parameter is significant and I should therefore use one of the …

I am trying to understand what "overdispersion" means in statistics. … References: https://en.wikipedia.org/wiki/Overdispersion …

I'm trying to get a handle on the concept of overdispersion in logistic regression. … I've read that overdispersion is when observed variance of a response variable is greater than would be expected from the binomial distribution. …

In the output, I see the following line : Overdispersion parameter for nbinom2 family (): 9.28e+06. How do I interpret such a large overdispersion? Please help. …

If the residual deviance is bigger than the residual degrees of freedom overdispersion is indicated. … My question is how can I check for overdispersion with the Gaussian distribution and how can I correct for it? …

R in Action (Kabacoff, 2011) suggests the following routine to test for overdispersion in a logistic regression: Fit logistic regression using binomial distribution: model_binom <- glm(Species=="versicolor … The p-value is 0.79 - how does this show that overdispersion is not a problem in the binomial distribution model? …

Does overdispersion mean anything when doing model selection and multi model inferences? … But does overdispersion means anything in Model selection inference? …

I ran some models for my count data, and did some diagnostics to check for overdispersion. Here is a dharma graph, which as I understand, indicates no overdispersion. … And this is the result I get when running overdisp(model1) dispersion ratio = 1.2987 Pearson's Chi-Squared = 496.1125 p-value = 0.0001 Overdispersion detected. …

I found that, because of some overdispersion in the data, a negative binomial regression seemed better than a poisson. … I was also wondering whether it is possible to test for overdispersion in SPSS in the multilevel context. …

I am struggling to understand overdispersion and when to check for overdispersion. Can you tell me in which case I should or should not check for overdispersion? …

interpreting the output from Ben Bolker's over-dispersion function (please see link below): https://bbolker.github.io/mixedmodels-misc/glmmFAQ.htmlhttps://bbolker.github.io/mixedmodels-misc/glmmFAQ.html Overdispersion … A second estimate is provided in the model itself Overdispersion parameter for nbinom2 family (): 11.3 …

I'm having trouble accounting for overdispersion in a binomial GLMER (lme4 package) - I'd read through other posts on the topic but haven't found anything that solves my problem. … So my first question is, is my dispersion value high enough that it's considered overdispersion (how black and white are those values)? …

Possible causes of overdispersion are Omitted variables Excess zero counts Correlation between individual responses Cluster sampling More... … Now there are several methods of testing for overdispersion including Auxiliary regression (in R) Likelihood ratio test (in R) These involve testing for overdispersion in a fitted model. …

One of the things I've read is that you can use beta-binomial regression to solve some overdispersion problems. … What I would like to know is: Is it reasonable to look for other distribution families (e.g. beta-binomial or quasibinomial) when overdispersion occurs? …

I understand that overdispersion indicates extra, unexplained variation in the response than would be expected based on the statistical model of choice. … But I don't have an intuition behind why the ratio of residual deviance to residual degrees of freedom gives a measure of overdispersion. …