Many SAS/STAT procedures, each with special
features, perform regression analysis.
The following procedures perform at
least one type of regression analysis:
- analyzes data that can be represented by a contingency table.
PROC CATMOD fits linear models to functions of response
frequencies, and it can be used for linear and logistic regression.
The CATMOD procedure is discussed in detail in Chapter 5, "Introduction to Categorical Data Analysis Procedures."
- fits generalized linear models. PROC GENMOD is especially
suited for responses with discrete outcomes, and it performs
logistic regression and Poisson regression as well as
fitting Generalized Estimating Equations for repeated
measures data. See Chapter 5, "Introduction to Categorical Data Analysis Procedures," and
Chapter 29, "The GENMOD Procedure," for more information.
- uses the method of least squares to fit general linear models.
In addition to many other analyses, PROC GLM can perform
simple, multiple, polynomial, and weighted regression.
PROC GLM has many of the same input/output capabilities
as PROC REG, but it does not provide as many diagnostic tools
or allow interactive changes in the model or data.
See Chapter 4, "Introduction to Analysis-of-Variance Procedures," for a more detailed overview of the GLM procedure.
- fits parametric models to failure-time
data that may be right censored.
These types of models are commonly used in survival analysis.
See Chapter 10, "Introduction to Survival Analysis Procedures," for a more
detailed overview of the LIFEREG procedure.
- fits logistic models for binomial and ordinal outcomes.
PROC LOGISTIC provides a wide variety of model-building
methods and computes numerous regression diagnostics.
See Chapter 5, "Introduction to Categorical Data Analysis Procedures,"
for a brief comparison of PROC LOGISTIC with other procedures.
- builds nonlinear regression models.
Several different iterative methods are available.
- performs regression using the
Gentleman-Givens computational method.
For ill-conditioned data, PROC ORTHOREG can produce more accurate
parameter estimates than other procedures such as PROC GLM and PROC REG.
- performs partial least squares regression, principal components
regression, and reduced rank regression, with cross validation for
the number of components.
- performs probit regression as well as logistic
regression and ordinal logistic regression.
The PROBIT procedure is useful when the dependent
variable is either dichotomous or polychotomous
and the independent variables are continuous.
- performs linear regression with many diagnostic capabilities,
selects models using one of nine methods, produces scatter plots
of raw data and statistics, highlights scatter plots to identify
particular observations, and allows interactive changes in
both the regression model and the data used to fit the model.
- builds quadratic response-surface regression models.
PROC RSREG analyzes the fitted response surface to determine
the factor levels of optimum response and performs a ridge
analysis to search for the region of optimum response.
- fits univariate and multivariate linear models, optionally
with spline and other nonlinear transformations. Models include ordinary
regression and ANOVA, multiple and multivariate regression, metric and
nonmetric conjoint analysis, metric and nonmetric vector and ideal point
preference mapping, redundancy analysis, canonical correlation, and
response surface regression.
Several SAS/ETS procedures also perform regression.
The following procedures are documented in the
SAS/ETS User's Guide.
- implements regression models using time-series
data where the errors are autocorrelated.
- performs regression analysis with polynomial distributed lags.
- handles linear simultaneous systems of
equations, such as econometric models.
- handles nonlinear simultaneous systems
of equations, such as econometric models.
Copyright © 1999 by SAS Institute Inc., Cary, NC, USA. All rights reserved.