The COUNTREG Procedure
- Overview
- Getting Started
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Syntax
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DetailsSpecification of RegressorsMissing ValuesPoisson RegressionNegative Binomial RegressionZero-Inflated Count Regression OverviewZero-Inflated Poisson RegressionZero-Inflated Negative Binomial RegressionVariable SelectionComputational ResourcesNonlinear Optimization OptionsCovariance Matrix TypesDisplayed OutputOUTPUT OUT= Data SetOUTEST= Data SetODS Table Names
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Examples
- References
Missing Values
Any observation in the input data set with a missing value for one or more of the regressors is ignored by PROC COUNTREG and not used in the model fit. PROC COUNTREG rounds any positive noninteger count values to the nearest integer. PROC COUNTREG ignores any observations with a negative count, a zero or negative weight, or a frequency less than 1.
If there are observations in the input data set with missing response values but with nonmissing regressors, PROC COUNTREG
can compute several statistics and store them in an output data set by using the OUTPUT statement. For example, you can request
that the output data set contain the estimates of 
, the expected value of the response variable, and the probability of the response variable taking on values that you specify.
In a zero-inflated model, you can additionally request that the output data set contain the estimates of 
, and the probability that the response is zero as a result of the zero-generating process. The presence of such observations
(with missing response values) does not affect the model fit.