The COUNTREG Procedure |
MODEL Statement |
The MODEL statement specifies the dependent variable and independent regressor variables for the regression model. The dependent count variable should take on only nonnegative integer values in the input data set. PROC COUNTREG rounds any positive noninteger count values to the nearest integer. PROC COUNTREG discards any observations with a negative count.
Only one MODEL statement can be specified. The following options can be used in the MODEL statement after a slash (/).
specifies a type of model to be analyzed. If you specify this option in both the MODEL statement and the PROC COUNTREG statement, then only the value in the MODEL statement is used. The supported model types as follows:
Poisson regression model
negative binomial regression model with a linear variance function
negative binomial regression model with a quadratic variance function
zero-inflated Poisson regression
zero-inflated negative binomial regression
specifies a variable in the input data set to be used as an offset variable. The offset variable appears as a covariate in the model with its parameter restricted to 1. The offset variable cannot be the response variable, the zero-inflation offset variable (if any), or one of the explanatory variables. The Model Fit Summary gives the name of the data set variable used as the offset variable; it is labeled as "Offset."
prints the correlation matrix of the parameter estimates. It can also be specified in the PROC COUNTREG statement.
prints the covariance matrix of the parameter estimates. It can also be specified in the PROC COUNTREG statement.
prints the objective function and parameter estimates at each iteration. The objective function is the negative log-likelihood function. It can also be specified in the PROC COUNTREG statement.
requests all printing options. It can also be specified in the PROC COUNTREG statement.
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