27
votes
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Identical coefficients estimated in Poisson vs Quasi-Poisson model
This is almost a duplicate; the linked question explains that you shouldn't expect the coefficient estimates, residual deviance, nor degrees of freedom to change. The only thing that changes when ...
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26
votes
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How to deal with overdispersion in Poisson regression: quasi-likelihood, negative binomial GLM, or subject-level random effect?
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 ...
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20
votes
Meaning of "Overdispersion" in Statistics
In a Poisson$(\lambda)$ distribution:
$$
\mu=\lambda\\
\sigma^2 =\lambda\\
\implies\\
\mu=\sigma^2
$$
Consequently, when we believe we have a Poisson distribution, we expect the samples drawn from it ...
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16
votes
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Are over-dispersion tests in GLMs actually *useful*?
In principle, I actually agree that 99% of the time, it's better to just use the more flexible model. With that said, here are two and a half arguments for why you might not.
(1) Less flexible means ...
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14
votes
The mean and variance of Poisson distribution are equal
Just read the next two sentences:
There is no way to increase the variance without increasing the mean. Unfortunately, in many data sets the variance is larger than the mean.
If you model some ...
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14
votes
Are over-dispersion tests in GLMs actually *useful*?
Although this is my own question, I'm also going to post my own two-cents as an answer, so that we add to the number of perspectives on this question. The issue here is whether or not it is sensible ...
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12
votes
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Adding an observation level random term messes up residuals vs fitted plot. Why?
Thanks for updating your post, Charly. I played with some over-dispersed Poisson data to see the impact of adding an observation level effect in the glmer model on the plot of residual versus fitted ...
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11
votes
Meaning of "Overdispersion" in Statistics
For many one-parameter probability distributions, the variance in the distribution is a function of the mean. When you fit data to a statistical model using these distributions, the estimator will ...
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10
votes
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Difference between heteroskedasticity and overdispersion
No, they are not equivalent. In fact, they are quite unrelated.
Heteroskedasticity is when variance differs between "situations". For instance, in a regression task, the variance of the ...
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8
votes
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Can you use glmmTMB to simultaneously model offsets and zero-inflation?
tl;dr as far as I can tell at this point,
...
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8
votes
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dispersion parameter in Poisson models
That's correct!
You've found out why glm doesn't use deviance/df as an estimate of dispersion: it's not a very good one. It uses the better estimate based on the ...
- 34.7k
7
votes
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Overdispersion in fitted generalized linear model with insignificant regression coefficients
Yes, that is true.
There are only two commonly-used generalized linear model families for which the concept of overdispersion is relevant. These are Poisson regression or binomial regression when the ...
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7
votes
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What better I use for Negative Binomial Regression with library(MASS) glm(family=negative.binomial) or glm.nb?
The negative binomial model is a generalized linear model only when the overdispersion parameter theta is known. In applications, we don't know it, and it needs to be estimated along with the other ...
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votes
Is there a test to determine whether GLM overdispersion is significant?
Yet another option would be to use a likelihood-ratio test to show that a quasipoisson GLM with overdispersion is significantly better than a regular poisson GLM without overdispersion :
...
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6
votes
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Machine Learning methods / Regression Trees for Longitudinal/Panel Count Data
The glmertree package on R-Forge (https://R-Forge.R-project.org/R/?group_id=261) extends the REEM tree approach in two directions: First, the response variable can ...
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6
votes
Overdispersion tests from DHARMa and sjstats: conflicting results?
I'm the developer of DHARMa. First of all: note that results are not actually conflicting - a non-significant test doesn't mean that there is no overdispersion, it just means just that the respective ...
- 7,729
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votes
Overdispersion in fitted generalized linear model with insignificant regression coefficients
Just to add to @GordonSmyth's answer, when you are fitting a quasipoisson or quasibinomial, the variance-covariance matrix is scaled by the dispersion value. This means the standard error of your ...
- 4,937
6
votes
Modelling count data with extreme underdispersion - what distribution?
The Conway-Maxwell-Poisson model has recently been shown to handle arbitrarily small underdispersion (see Huang 2020). For example, it is possible to have a mean of 15 and a variance of 2, say, by ...
- 61
5
votes
Is there a test to determine whether GLM overdispersion is significant?
Another alternative is to use the P__disp function from the msme package. The P__disp ...
5
votes
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How to check overdispersion of binomial GLMMs, lme4 package
Why are the variance and Std.Dev of the random effects zero?
Because the marginal variance among sites in your data is less than would be expected from a binomial variable; the variance can't be ...
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5
votes
Can a model fitted using negative binomial distribution be over dispersed?
Answer to question 1)
With count data "overdispersion" usually means relative to a Poisson distribution, but one could also say "overdispersed relative to a negative binomial distribution", meaning ...
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5
votes
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Quasi-likelihood/Quasi Poisson
What happens is that the likelihood equations depend on the distribution of Y only through the mean ($\mu$) and the variance ($V(\mu)$). Other moments of the distribution do not affect the ...
- 531
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votes
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Investigate overdispersion in a plot for a poisson regression
One somewhat useful plot would be to plot absolute Pearson residuals against $\sqrt{\hat{y}}$ (or $\hat{y}$ or $\log(\hat{y})$...). It should look flat, and as long as the fitted mean isn't too small ...
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5
votes
Why fitting a Poisson GLM in an over dispersed dataset underestimate the standard error of the regression parameter?
As a result of the Poisson probability model, the variance and the mean of that distribution are the same. This fact entails that whenever this condition is not met, there will be a mismatch between ...
- 1,468
4
votes
Checking for overdispersion in a Poisson model
I am not familiar with those packages, nor the outputs. However, if you can identify the residual deviance and the residual degrees of freedom in the output, the two should be roughly equal or else ...
- 576
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votes
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Can there be overdispersion in a logistic regression model where each observation represents a single Bernoulli trial?
If the observations are independent it is impossible to have overdispersion with Bernoulli responses. I'm not sure what made the question arise. If the regression model is improperly specified you ...
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4
votes
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Is Quasi-Poisson the same thing as fitting a Poisson GEE model?
Quasi-poisson GLM is a special case of Poisson GEE.
The specification of GEE (copied from wikipedia) is that
$$U(\beta)=\sum_i \frac{\partial\mu_{ij}}{\partial \beta_k} V_i(Y_i-\mu_i(\beta))$$
...
- 1,374
4
votes
Difference between Quasi-Poisson and Sandwich Covariance
From a purely applied perspective, my experience is that the difference between these methods is typically not huge, leading to qualitatively the same conclusions (see Table 2 in the vignette you ...
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4
votes
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When do I have to check for overdispersion?
Overdispersion means more (higher) dispersion than assumed by the model so is a concept that is relative, it depends on the model used. Many (most) models do not assume anything about the ...
- 74.7k
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