The COUNTREG Procedure
- Overview
- Getting Started
-
Syntax
Functional SummaryPROC COUNTREG StatementBAYES StatementBOUNDS StatementBY StatementCLASS StatementDISPMODEL StatementFREQ StatementINIT StatementMODEL StatementNLOPTIONS StatementOUTPUT StatementPERFORMANCE StatementPRIOR StatementRESTRICT StatementSCORE StatementSHOW StatementSTORE StatementWEIGHT StatementZEROMODEL Statement -
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 AnalysisBY Groups and Scoring with an Item StoreComputational ResourcesNonlinear Optimization OptionsCovariance Matrix TypesDisplayed OutputBayesian AnalysisPrior DistributionsAutomated MCMC AlgorithmOUTPUT OUT= Data SetOUTEST= Data SetODS Table NamesODS Graphics
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Examples
- References
The OUTPUT statement creates a new SAS data set that contains all the variables in the input data set and, optionally, the
estimates of 
, the expected value of the response variable, and the probability that the response variable will take the current value
or other values that you specify. In a zero-inflated model, you can also 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. In a Conway-Maxwell-Poisson model,
you can also request that the output data set contains estimates of 
, 
, 
, 
, mode, variance and dispersion.
Except for the probability of the current value, these statistics can be computed for all observations in which the regressors are not missing, even if the response is missing. By adding observations that have missing response values to the input data set, you can compute these statistics for new observations or for settings of the regressors that are not present in the data without affecting the model fit.