The COUNTREG Procedure
- Overview
- Getting Started
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Syntax
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DetailsSpecification of RegressorsMissing ValuesPoisson RegressionConway-Maxwell-Poisson RegressionNegative Binomial RegressionZero-Inflated Count Regression OverviewZero-Inflated Poisson RegressionZero-Inflated Conway-Maxwell-Poisson RegressionZero-Inflated Negative Binomial RegressionVariable SelectionPanel Data AnalysisComputational ResourcesNonlinear Optimization OptionsCovariance Matrix TypesDisplayed OutputOUTPUT OUT= Data SetOUTEST= Data SetODS Table NamesODS Graphics
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Examples
- References
Any observation in the input data set that has 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 that have a negative count, a zero or negative weight, or a frequency less than 1.
If there are observations in the input data set that have 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 that the response variable will take 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.