The COUNTREG Procedure

Functional Summary

Table 11.1 summarizes statements and that you can use in the COUNTREG procedure.

Table 11.1: PROC COUNTREG Functional Summary

Description	Statement	Option
Data Set Options
Specifies the input data set	COUNTREG	DATA=
Specifies the identification variable for panel data analysis	COUNTREG	GROUPID=
Writes parameter estimates to an output data set	COUNTREG	OUTEST=
Requests that the procedure produce graphics via the Output Delivery System	COUNTREG	PLOTS=
Writes estimates to an output data set	OUTPUT	OUT=
Declaring the Role of Variables
Specifies BY-group processing	BY
Specifies classification variables	CLASS
Specifies a frequency variable	FREQ
Specifies a weight variable	WEIGHT
Item Store Control Options
Displays the contents of the item store	SHOW
Stores the model in an item store	STORE
Restores the model from the item store	COUNTREG	RESTORE=
Printing Control Options
Prints the correlation matrix of the estimates	MODEL	CORRB
Prints the covariance matrix of the estimates	MODEL	COVB
Prints a summary iteration listing	MODEL	ITPRINT
Suppresses the normal printed output	COUNTREG	NOPRINT
Requests all printing options	MODEL	PRINTALL
Option Process Control Options
Specifies maximum number of iterations allowed	MODEL	MAXITER=
Selects the iterative minimization method to use	COUNTREG	METHOD=
Sets boundary restrictions on parameters	BOUNDS
Sets initial values for parameters	INIT
Sets linear restrictions on parameters	RESTRICT
Sets the number of threads to use	PERFORMANCE
Specifies the optimization options	NLOPTIONS	See Chapter 6: Nonlinear Optimization Methods
Model Estimation Options
Specifies the dispersion variables	DISPMODEL
Specifies the type of model	COUNTREG	DIST=
Specifies the type of covariance matrix	MODEL	COVEST=
Specifies the type of error components model for panel data	MODEL	ERRORCOMP=
Suppresses the intercept parameter	MODEL	NOINT
Specifies the offset variable	MODEL	OFFSET=
Specifies the parameterization for Conway-Maxwell-Poisson (CMP) model	MODEL	PARAMETER=
Specifies the zero-inflated offset variable	ZEROMODEL	OFFSET=
Specifies the zero-inflated link function	ZEROMODEL	LINK=
Specifies variable selection	MODEL	SELECT=( )
Bayesian MCMC Options
Controls the aggregation of multiple posterior chains	BAYES	AGGREGATION=
Automates the initialization of the MCMC algorithm	BAYES	AUTOMCMC()
Specifies the initial values of the MCMC algorithm	INIT
Specifies the maximum number of tuning phases	BAYES	MAXTUNE=
Specifies the minimum number of tuning phases	BAYES	MINTUNE=
Specifies the number of burn-in iterations	BAYES	NBI=
Specifies the number of iterations during the sampling phase	BAYES	NMC=
Specifies the number of threads to use during the sampling phase	BAYES	NTRDS=
Specifies the number of iterations during the tuning phase	BAYES	NTU=
Controls options for constructing the initial proposal covariance matrix	BAYES	PROPCOV=
Specifies the sampling scheme	BAYES	SAMPLING=
Specifies the random number generator seed	BAYES	SEED=
Prints the time required for the MCMC sampling	BAYES	SIMTIME
Controls the thinning of the Markov chain	BAYES	THIN=
Bayesian Summary Statistics and Convergence Diagnostics
Displays convergence diagnostics	BAYES	DIAGNOSTICS=
Displays summary statistics of the posterior samples	BAYES	STATISTICS=
Bayesian Prior and Posterior Samples
Specifies a SAS data set for the posterior samples	BAYES	OUTPOST=
Bayesian Analysis
Specifies normal prior distribution	PRIOR	NORMAL (MEAN=, VAR=)
Specifies gamma prior distribution	PRIOR	GAMMA (SHAPE=, SCALE=)
Specifies inverse gamma prior distribution	PRIOR	IGAMMA (SHAPE=, SCALE=)
Specifies uniform prior distribution	PRIOR	UNIFORM (MIN=, MAX=)
Specifies beta prior distribution	PRIOR	BETA (SHAPE1=, SHAPE2=, MIN=, MAX=)
Specifies t prior distribution	PRIOR	T (LOCATION=, DF=)
Output Control Options
Includes covariances in the OUTEST= data set	COUNTREG	COVOUT
Outputs the estimates of dispersion for the CMP model	OUTPUT	DISPERSION
Outputs the estimates of GDelta $=\mathbf{g}_{i}’\bdelta$ for CMP model	OUTPUT	GDELTA=
Outputs the estimates of $\lambda$ for the CMP model	OUTPUT	LAMBDA=
Outputs the estimates of $\nu$ for the CMP model	OUTPUT	NU=
Outputs the estimates of $\mu$ for the CMP model	OUTPUT	MU=
Outputs the estimates of mode for the CMP model	OUTPUT	MODE=
Outputs the probability that the response variable will take the current value	OUTPUT	PROB=
Outputs probabilities for particular response values	OUTPUT	PROBCOUNT( )
Outputs expected value of response variable	OUTPUT	PRED=
Outputs the estimates of variance for the CMP model	OUTPUT	VARIANCE=
Outputs estimates of XBeta $=\mathbf{x}_{i}’\bbeta$	OUTPUT	XBETA=
Outputs estimates of ZGamma $=\mathbf{z}_{i}’\bgamma$	OUTPUT	ZGAMMA=
Outputs the probability that the response variable will take a zero value as a result of the zero-generating process	OUTPUT	PROBZERO=
Specifies the output data set for scoring	SCORE	OUT=
Outputs the estimates of dispersion for the CMP model	SCORE	DISPERSION
Outputs the estimates of GDelta $=\mathbf{g}_{i}’\bdelta$ for the CMP model	SCORE	GDELTA=
Outputs the estimates of $\lambda$ for the CMP model	SCORE	LAMBDA=
Outputs the estimates of $\nu$ for the CMP model	SCORE	NU=
Outputs the estimates of $\mu$ for the CMP model	SCORE	MU=
Outputs the estimates of mode for the CMP model	SCORE	MODE=
Outputs the probability that the response variable will take the current value	SCORE	PROB=
Outputs probabilities for particular response values	SCORE	PROBCOUNT( )
Outputs expected value of response variable	SCORE	PRED=
Outputs the estimates of variance for the CMP model	SCORE	VARIANCE=
Outputs estimates of XBeta $=\mathbf{x}_{i}’\bbeta$	SCORE	XBETA=
Outputs estimates of ZGamma $=\mathbf{z}_{i}’\bgamma$	SCORE	ZGAMMA=
Outputs the probability that the response variable will take a zero value as a result of the zero-generating process	SCORE	PROBZERO=