Count Data Regression Task

About the Count Data Regression Task

Count data regression fits regression models where the dependent variable has nonnegative integer or count values.

Note: The version of the task depends on what version of SAS/ETS is available at your site. For example, if your site is running SAS 9.4 (or earlier), SAS Studio is running version 1 of the Count Data Regression task. If you are running the first maintenance release for SAS 9.4, SAS/ETS 13.1 is available, and SAS Studio is running version 2 of the Count Data Regression task. The difference between the two versions is the addition of new options in SAS/ETS 13.1.

Example: Count Data Regression

To create this example:

Create the WORK.LONG97DATA data set. For more information, see LONG97DATA Data Set.
In the Tasks section, expand the Econometrics folder and double-click Count Data Regression. The user interface for the Count Data Regression task opens.
On the Data tab, select the WORK.LONG97DATA data set.

Assign columns to these roles:

Role	Column Name
Dependent variable	art
Continuous variables	ment phd mar
Categorical variables	kid5

To run the task, click .

Here is a subset of the results:

Assigning Data to Roles

To run the Count Data Regression task, you must assign a column to the Dependent variable role.

Role	Description
Dependent variable	specifies the numeric column that has nonnegative integer or count values. The Distribution option specifies the type of model to be analyzed. You can specify these types of models: Poisson regression model negative binomial regression model with a linear variance function negative binomial regression model with a quadratic variance function a zero-inflated Poisson model a zero-inflated negative binomial model
Continuous variables	specifies the independent covariates (regressors) for the regression model. If you do not specify a continuous variable, the task fits a model that contains only an intercept.
Categorical variables	specifies the variables to use to group data in the analysis.

Setting Options

Option	Description
Methods
Type of covariances of the parameter estimates	specifies the type of covariance matrix of the parameter estimates. You can specify these types of matrices: the covariance from the inverse Hessian matrix the covariance from the outer product mix the covariance from the outer product and Hessian matrices (also called the quasi-maximum-likelihood-estimates)
Include the intercept in the model	specifies whether to include the intercept in the model.
Optimization
Method	specifies the iterative minimization method to use.
Maximum number of iterations	specifies the maximum number of iterations for the selected method.
Plots Note: The plot options are available only if you are running the first maintenance release for SAS 9.4.
Diagnostic Plots
Profile likelihood plot	produces the profile likelihood functions of the model parameters. The model parameter on the X axis is varied, whereas all other parameters are fixed at their estimated maximum likelihood estimates.
Overdispersion diagnostic plot	produces the overdispersion diagnostic plot.
Probability Plots
Specified count levels	supplies the values of the response variable for the overall predictive probabilities plot and the predictive probability profiles plot. Each value should be a nonnegative integer. Nonintegers are rounded to the nearest integer. This value can also be a list in the form X TO Y BY Z. For example, COUNTS(0 1 2 TO 10 BY 2 15) creates a plot for counts 0, 1, 2, 4, 6, 8, 10, and 15.
Overall predictive probabilities plot	produces the overall predictive probabilities of the specified count levels.
Predictive probability profiles plot	produces the predictive probability profiles of specified count levels against model regressors. The regressor on the X axis is varied, whereas all other regressors are fixed at the mean of the observed data set.
Zero-inflation Plots
Probability profiles plot of zero-inflation process selection	produces the probability profiles of zero-inflation process selection and zero count prediction against model regressors. The regressor on the X axis is varied, whereas all other regressors are fixed at the mean of the observed data set.
Display plots	specifies whether to display the plots in a panel or individually.
Output Tables
You can specify whether to include any output tables in the results. Here is the information that you can include in the results: correlation matrix of the parameter estimates covariance matrix of the parameter estimates iteration history of the objective function and parameter estimates