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predicted count

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

by Jeff Meyer 7 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

Getting Accurate Predicted Counts When There Are No Zeros in the Data

by Jeff Meyer Leave a Comment

We previously examined why a linear regression and negative binomial regression were not viable models for predicting the expected length of stay in the hospital for people with the flu.  A linear regression model was not appropriate because our outcome variable, length of stay, was discrete and not continuous.

A negative binomial model wasn’t the proper choice because the minimum length of stay is not zero. The minimum length of stay is one day. Negative binomial and Poisson models can only be used on data where the observations’ outcome have the possibility of having a zero count.

We need to use a truncated negative binomial model to analyze the expected length of stay of people admitted to the hospital who have the flu. Calculating the expected length of stay is an easy task once we create our model. [Read more…] about Getting Accurate Predicted Counts When There Are No Zeros in the Data

Tagged With: conditional mean, Count data, incidence rate ratio, linear regression, Negative Binomial Regression, predicted count, truncated negative binomial model, Zero Truncated

Related Posts

  • The Importance of Including an Exposure Variable in Count Models
  • Count Models: Understanding the Log Link Function
  • The Problem with Linear Regression for Count Data
  • A Few Resources on Zero-Inflated Poisson Models

The Problem with Linear Regression for Count Data

by Jeff Meyer Leave a Comment

Imagine this scenario:

This year’s flu strain is very vigorous. The number of people checking in at hospitals is rapidly increasing. Hospitals are desperate to know if they have enough beds to handle those who need their help.

You have been asked to analyze a previous year’s hospitalization length of stay by people with the flu who had been admitted to the hospital. The predictors in your data set are age group, gender and race of those admitted. You also have an indicator that signifies whether the hospital was privately or publicly run.

[Read more…] about The Problem with Linear Regression for Count Data

Tagged With: Count data, count model, linear regression, negative binomial, Poisson Regression, predicted count, Truncated

Related Posts

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

Two-Way Tables and Count Models: Expected and Predicted Counts

by Jeff Meyer Leave a Comment

by Jeff Meyer

In a previous article, we discussed how incidence rate ratios calculated in a Poisson regression can be determined from a two-way table of categorical variables.

Statistical software can also calculate the expected (aka predicted) count for each group. Below is the actual and expected count of the number of boys and girls participating and not participating in organized sports.

cm-twowaytables-1

 

 

 

 

 

 

 

The value in the top of each cell is the actual count (40 boys do not play organized sports) and the bottom value is the expected/predicted count (36 boys are predicted to not play organized sports).

The Poisson model that we ran in the previous article generated the following table: [Read more…] about Two-Way Tables and Count Models: Expected and Predicted Counts

Tagged With: categorical variable, expected count, poisson, predicted count, two-way table

Related Posts

  • Poisson or Negative Binomial? Using Count Model Diagnostics to Select a Model
  • Getting Accurate Predicted Counts When There Are No Zeros in the Data
  • The Problem with Linear Regression for Count Data
  • Analyzing Zero-Truncated Count Data: Length of Stay in the ICU for Flu Victims

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