Overview
The REG procedure is one of many regression procedures in the SAS System.
It is a general-purpose procedure for regression, while other
SAS regression procedures provide more specialized applications.
Other SAS/STAT procedures that perform at least one type of
regression analysis are the CATMOD, GENMOD, GLM, LOGISTIC, MIXED, NLIN,
ORTHOREG, PROBIT, RSREG, and TRANSREG procedures.
SAS/ETS procedures are specialized for applications
in time-series or simultaneous systems.
These other SAS/STAT regression procedures
are summarized in Chapter 3, "Introduction to Regression Procedures,"
which also contains an overview of
regression techniques and defines many of the statistics
computed by PROC REG and other regression procedures.
PROC REG provides the following capabilities:
- multiple MODEL statements
- nine model-selection methods
- interactive changes both in the
model and the data used to fit the model
- linear equality restrictions on parameters
- tests of linear hypotheses and multivariate hypotheses
- collinearity diagnostics
- predicted values, residuals, studentized
residuals, confidence limits, and influence statistics
- correlation or crossproduct input
- requested statistics available for output through
output data sets
- plots
- -
- plot model fit summary statistics and
diagnostic statistics
- -
- produce normal quantile-quantile (Q-Q) and
probability-probability (P-P) plots for statistics such as residuals
- -
- specify special shorthand options to
plot ridge traces, confidence intervals, and prediction intervals
- -
- display the fitted model equation, summary statistics, and
reference lines on the plot
- -
- control the graphics appearance with
PLOT statement options and with global graphics statements including
the TITLE, FOOTNOTE, NOTE, SYMBOL, and LEGEND statements
- -
- "paint" or highlight line-printer
scatter plots
- -
- produce partial regression leverage line-printer plots
Nine model-selection methods are available in PROC REG.
In the simplest method,
PROC REG fits the complete model that you specify.
The other eight methods involve various ways of
including or excluding variables from the model.
You specify these methods with the
SELECTION= option in the MODEL statement.
The methods are identified in the following list and are explained in detail
in the "Model-Selection Methods" section.
- NONE
- no model selection.
This is the default.
The complete model specified in the
MODEL statement is fit to the data.
- FORWARD
- forward selection.
This method starts with no variables
in the model and adds variables.
- BACKWARD
- backward elimination.
This method starts with all variables
in the model and deletes variables.
- STEPWISE
- stepwise regression.
This is similar to the FORWARD method except that variables
already in the model do not necessarily stay there.
- MAXR
- forward selection to fit the best one-variable
model, the best two-variable model, and so on.
Variables are switched so that R2 is maximized.
- MINR
- similar to the MAXR method, except that variables are switched so that the
increase in R2 from adding a variable to the model is minimized.
- RSQUARE
- finds a specified number of models with the
highest R2 in a range of model sizes.
- ADJRSQ
- finds a specified number of models with the
highest adjusted R2 in a range of model sizes.
- CP
- finds a specified number of models with
the lowest Cp in a range of model sizes.
Copyright © 1999 by SAS Institute Inc., Cary, NC, USA. All rights reserved.
