COUNTREG Procedure
Functional Summary
Table 1 summarizes the statements that you can use in the COUNTREG procedure.
Table 1: PROC COUNTREG Functional Summary
| Description | Statement | Option |
|---|---|---|
| Data Set Options | ||
| Specifies the input data set | PROC COUNTREG | DATA= |
| Specifies the input spatial weights data set | PROC COUNTREG | WMAT= |
| Specifies the identification variable for panel data analysis | PROC COUNTREG | GROUPID= |
| Does not row-normalize the spatial weights matrix | PROC COUNTREG | NONORMALIZE |
| Writes parameter estimates to an output data set | PROC COUNTREG | OUTEST= |
| Requests that the procedure produce graphics via the Output Delivery System | PROC 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 | |
| Specifies a spatial ID variable | SPATIALID | |
| 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 | PROC 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 | PROC 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 | PROC 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 | PROC 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 the 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=( ) |
| Specifies variable selection | DISPMODEL | SELECT=( ) |
| Specifies variable selection | ZEROMODEL | SELECT=( ) |
| Specifies the spatial effects to be added to MODEL statement | SPATIALEFFECTS | |
| Specifies variable selection | SPATIALEFFECTS | SELECT=( ) |
| Specifies the spatial effects for dispersion | SPATIALDISPEFFECTS | |
| Specifies variable selection | SPATIALDISPEFFECTS | SELECT=( ) |
| Specifies the spatial effects for zero-inflation | SPATIALZEROEFFECTS | |
| Specifies variable selection | SPATIALZEROEFFECTS | 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 | |
| Requests evaluation of the marginal likelihood | BAYES | MARGINLIKE |
| 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 | PROC COUNTREG | COVOUT |
| Outputs the estimates of dispersion for the CMP model | OUTPUT | DISPERSION |
Outputs the estimates of  for the CMP model | OUTPUT | GDELTA= |
Outputs the estimates of  for the CMP model | OUTPUT | LAMBDA= |
Outputs the estimates of  for the CMP model | OUTPUT | NU= |
Outputs the estimates of  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 the expected value of the response variable | OUTPUT | PRED= |
| Outputs the estimates of variance for the CMP model | OUTPUT | VARIANCE= |
Outputs estimates of  | OUTPUT | XBETA= |
Outputs estimates of  | 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 for the CMP model | SCORE | GDELTA= |
| Outputs the estimates of for the CMP model | SCORE | LAMBDA= |
| Outputs the estimates of for the CMP model | SCORE | NU= |
| Outputs the estimates of 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 | SCORE | XBETA= |
| Outputs estimates of | SCORE | ZGAMMA= |
| Outputs the probability that the response variable will take a value of zero as a result of the zero-generating process | SCORE | PROBZERO= |
| Test Request Options | ||
| Requests Wald, Lagrange multiplier, and likelihood ratio tests | TEST | ALL |
| Requests the Wald test | TEST | WALD |
| Requests the Lagrange multiplier test | TEST | LM |
| Requests the likelihood ratio test | TEST | LR |
Last updated: June 19, 2025