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overdispersion

Poisson or Negative Binomial? Using Count Model Diagnostics to Select a Model

by Jeff Meyer 10 Comments

How do you choose between Poisson and negative binomial models for discrete count outcomes?

One key criterion is the relative value of the variance to the mean after accounting for the effect of the predictors. A previous article discussed the concept of a variance that is larger than the model assumes: overdispersion.

(Underdispersion is also possible, but much less common).

There are two ways to check for overdispersion: [Read more…] about Poisson or Negative Binomial? Using Count Model Diagnostics to Select a Model

Tagged With: count model, dispersion statistic, Model Fit, negative binomial, overdispersion, poisson, predicted count, residual plot

Related Posts

  • Overdispersion in Count Models: Fit the Model to the Data, Don’t Fit the Data to the Model
  • The Problem with Linear Regression for Count Data
  • The Importance of Including an Exposure Variable in Count Models
  • Analyzing Zero-Truncated Count Data: Length of Stay in the ICU for Flu Victims

Overdispersion in Count Models: Fit the Model to the Data, Don’t Fit the Data to the Model

by Jeff Meyer 2 Comments

by Jeff Meyer

If you have count data you use a Poisson model for the analysis, right?

The key criterion for using a Poisson model is after accounting for the effect of predictors, the mean must equal the variance. If the mean doesn’t equal the variance then all we have to do is transform the data or tweak the model, correct?

Let’s see how we can do this with some real data. A survey was done in Australia during the peak of the flu season. The outcome variable is the total number of times people asked for medical advice from any source over a two-week period.

We are trying to determine what influences people with flu symptoms to seek medical advice. The mean number of times was 0.516 times and the variance 1.79.

The mean does not equal the variance even after accounting for the model’s predictors.

Here are the results for this model: [Read more…] about Overdispersion in Count Models: Fit the Model to the Data, Don’t Fit the Data to the Model

Tagged With: count model, negative binomial, overdispersion, poisson

Related Posts

  • Poisson or Negative Binomial? Using Count Model Diagnostics to Select a Model
  • The Importance of Including an Exposure Variable in Count Models
  • The Problem with Linear Regression for Count Data
  • Analyzing Zero-Truncated Count Data: Length of Stay in the ICU for Flu Victims

Generalized Linear Models in R, Part 7: Checking for Overdispersion in Count Regression

by guest contributer 9 Comments

by David Lillis, Ph.D.

In my last blog post we fitted a generalized linear model to count data using a Poisson error structure.

We found, however, that there was over-dispersion in the data – the variance was larger than the mean in our dependent variable.

[Read more…] about Generalized Linear Models in R, Part 7: Checking for Overdispersion in Count Regression

Tagged With: count regression, count variable, generalized linear models, GLM, overdispersion, Poisson Regression, R

Related Posts

  • Generalized Linear Models in R, Part 6: Poisson Regression for Count Variables
  • Generalized Linear Models (GLMs) in R, Part 4: Options, Link Functions, and Interpretation
  • Generalized Linear Models in R, Part 5: Graphs for Logistic Regression
  • Why Generalized Linear Models Have No Error Term

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