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
-
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 of the response variable taking on the current value or
other 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. For the Conway-Maxwell-Poisson
model, the estimates of 
, 
, 
, 
, mode, variance, and dispersion are also available. 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.
You can specify only one OUTPUT statement. You can specify the following output-options:
- OUT=SAS-data-set
- XBETA=name
-
PRED=name
MEAN=name -
assigns a name to the variable that contains the predicted value of the response variable.
- PROB=name
-
names the variable that contains the probability of the response variable taking the current value, Pr(
).
- PROBCOUNT(value1 <value2...>)
-
outputs the probability that the response variable will take particular values. Each value should be a nonnegative integer. Nonintegers are rounded to the nearest integer. The value can also be a list of the form X TO Y BY Z. For example, PROBCOUNT(0 1 2 TO 10 BY 2 15) requests predicted probabilities for counts 0, 1, 2, 4, 5, 6, 8, 10, and 15. This option is not available for the fixed-effects and random-effects panel models.
- ZGAMMA=name
- PROBZERO=name
-
names the variable that contains the value of
, the probability of the response variable taking on the value of zero as a result of the zero-generating process. It is written
to the output file only if the model is zero-inflated. This is not the overall probability of a zero response; that is provided
by the PROBCOUNT(0) option.
- GDELTA=name
-
assigns a name to the variable that contains estimates of
for the Conway-Maxwell-Poisson distribution.
- LAMBDA=name
-
assigns a name to the variable that contains the estimate of
for the Conway-Maxwell-Poisson distribution.
- NU=name
-
assigns a name to the variable that contains the estimate of
for the Conway-Maxwell-Poisson distribution.
- MU=name
-
assigns a name to the variable that contains the estimate of
for the Conway-Maxwell-Poisson distribution.
- MODE=name
-
assigns a name to the variable that contains the integral part of
(mode) for the Conway-Maxwell-Poisson distribution.
- VARIANCE=name
-
assigns a name to the variable that contains the estimate of variance for the Conway-Maxwell-Poisson distribution.
- DISPERSION=name
-
assigns a name to the variable that contains the value of dispersion for the Conway-Maxwell-Poisson distribution.