The HPCOUNTREG Procedure

PROC HPCOUNTREG Features

The HPCOUNTREG procedure estimates the parameters of a count regression model by maximum likelihood techniques.

The HPCOUNTREG procedure supports the following models for count data:

  • Poisson regression

  • Conway-Maxwell-Poisson regression

  • negative binomial regression with quadratic and linear variance functions (Cameron and Trivedi 1986)

  • zero-inflated Poisson (ZIP) model (Lambert 1992)

  • zero-inflated Conway-Maxwell-Poisson (ZICMP) model

  • zero-inflated negative binomial (ZINB) model

  • fixed-effects and random-effects Poisson models for panel data

  • fixed-effects and random-effects negative binomial models for panel data

The following list summarizes some basic features of the HPCOUNTREG procedure:

  • can perform analysis on a massively parallel high-performance appliance

  • reads input data in parallel and writes output data in parallel when the data source is the appliance database

  • is highly multithreaded during all phases of analytic execution

  • has model-building syntax that uses CLASS and effect-based MODEL statements familiar from SAS/ETS analytic procedures

  • performs maximum likelihood estimation

  • supports multiple link functions

  • uses the WEIGHT statement for weighted analysis

  • uses the FREQ statement for grouped analysis

  • uses the OUTPUT statement to produce a data set that contains predicted probabilities and other observationwise statistics