Chapter 63: The RSREG Procedure


Overview

The RSREG procedure uses the method of least squares to fit quadratic response surface regression models. Response surface models are a kind of general linear model in which attention focuses on characteristics of the fit response function and in particular, where optimum estimated response values occur.

In addition to fitting a quadratic function, you can use the RSREG procedure to

  • test for lack of fit

  • test for the significance of individual factors

  • analyze the canonical structure of the estimated response surface

  • compute the ridge of optimum response

  • predict new values of the response

Comparison to Other SAS Software

Other SAS/STAT procedures can be used to fit the response surface, but the RSREG procedure is more specialized. The following statements model a three-factor response surface in PROC RSREG:

  proc rsreg;   model y=x1 x2 x3;   run;  

These statements are more compact than the statements for other regression procedures in SAS/STAT software. For example, the equivalent statements for the GLM procedure are

  proc glm;   model y=x1 x1*x1   x2 x1*x2 x2*x2   x3 x1*x3 x2*x3 x3*x3;   run;  

Additionally, PROC RSREG includes specialized methodology for analyzing the fitted response surface, such as canonical analysis and optimum response ridges.

Note that the ADX Interface in SAS/QC software provides an interactive environment for constructing and analyzing many different kinds of experiments, including response surface experiments. The ADX Interface is the preferred interactive SAS System tool for analyzing experiments, since it includes facilities for checking underlying assumptions and graphically optimizing the response surface. The RSREG procedure is appropriate for analyzing experiments in a batch environment.

Terminology

Variables are referred to according to the following conventions:

factor variables

independent variables used in constructing the quadratic response surface. To estimate the necessary parameters, each variable must have at least three distinct values in the data. Independent variables must be numeric.

response variables

the dependent variables to which the quadratic response surface is fit. Dependent variables must be numeric.

covariates

additional independent variables for use in the regression but not in the formation of the quadratic response surface. Covariates must be numeric.

WEIGHT variable

a variable for weighting the observations in the regression. The WEIGHT variable must be numeric.

ID variables

variables not in the above lists that are transferred to an output data set containing statistics for each observation in the input data set. This data set is created using the OUT= option in the PROC RSREG statement. ID variables can be either character or numeric.

BY variables

variables for grouping observations. Separate analyses are obtained for each BY group . BY variables can be either character or numeric.




SAS.STAT 9.1 Users Guide (Vol. 6)
SAS.STAT 9.1 Users Guide (Vol. 6)
ISBN: N/A
EAN: N/A
Year: 2004
Pages: 127

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