The LOGSELECT Procedure
PROC LOGSELECT Features
The LOGSELECT procedure estimates the parameters of a logistic regression model by using maximum likelihood techniques. It also does the following:
provides model-building syntax with the CLASS, EFFECT, and effect-based MODEL statements, which are familiar from SAS/STAT analytic procedures (in particular, the GLM, LOGISTIC, GLIMMIX, and MIXED procedures)
provides response-variable options as in the LOGISTIC procedure
performs maximum likelihood estimation
provides the logit, probit, log-log, and complementary log-log link functions
provides cumulative link models for ordinal response data and generalized logit modeling for unordered multinomial data
enables model building (variable selection) through the SELECTION statement
provides a WEIGHT statement for weighted analysis
provides a FREQ statement for grouped analysis
provides a CODE statement to produce SAS code that can score a new data set
provides an OUTPUT statement to produce a data table that contains predicted probabilities and other observationwise statistics
uses ODS Graphics to create model selection plots as part of its output. For more information about ODS Graphics, see the section ODS Graphics.
Because the LOGSELECT procedure runs on CAS, it also does the following:
enables you to run on a cluster of machines that distribute the data and the computations
enables you to run in single-machine mode on CAS
exploits all the available cores and concurrent threads. For information about how PROC LOGSELECT uses threads, see the section Multithreading in Chapter 2, Shared Concepts.