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 time and memory that PROC COUNTREG requires are proportional to the number of parameters in the model and the number of observations in the data set being analyzed. Less time and memory are required for smaller models and fewer observations. Also affecting these resources are the method that is chosen to calculate the variance-covariance matrix and the optimization method. All optimization methods available through the METHOD= option have similar memory use requirements.
The processing time might differ for each method, depending on the number of iterations and functional calls needed. The data
set is read into memory to save processing time. If not enough memory is available to hold the data, the COUNTREG procedure
stores the data in a utility file on disk and rereads the data as needed from this file. When this occurs, the execution time
of the procedure increases substantially. The gradient and the variance-covariance matrix must be held in memory. If the model
has p parameters including the intercept, then at least 
bytes are needed. If the quasi-maximum likelihood method is used to estimate the variance-covariance matrix (COVEST=QML),
an additional 
bytes of memory are needed.
Time is also a function of the number of iterations needed to converge to a solution for the model parameters. The number of iterations that are needed cannot be known in advance. The MAXITER= option can be used to limit the number of iterations that PROC COUNTREG does. The convergence criteria can be altered by nonlinear optimization options available in the PROC COUNTREG statement. For a list of all the nonlinear optimization options, see Chapter 6: Nonlinear Optimization Methods.