MODEL Statement
 MODEL responses=independents < / options >
;
The MODEL statement lists response (dependent)
variables followed by an equal sign and then lists
independent variables, some of which may be covariates.
The output options to the MODEL statement specify
which statistics are output to the data set created
using the OUT= option in the PROC RSREG statement.
If none of the options are selected, the data
set is created but contains no observations.
The option keywords become values of the special
variable _TYPE_ in the output data set.
Any of the following options can be specified:
Task


Options

Analyze Original Data   NOCODE 
Fit Model to First BY Group Only   BYOUT 
Declare Covariates   COVAR= 
Request Additional Statistics   PRESS 
Request Additional Tests   LACKFIT 
Suppress Displayed Output   NOANOVA 
  NOOPTIMAL 
  NOPRINT 
Output Statistics   ACTUAL 
  PREDICT 
  RESIDUAL 
  L95 
  U95 
  L95M 
  U95M 
  D 
 ACTUAL

specifies that the observed response values from the input data set
be written to the output data set.
 BYOUT

uses only the first BY group to estimate the model.
Subsequent BY groups have scoring statistics
computed in the output data set only.
The BYOUT option is used only when a BY statement is specified.
 COVAR=n

declares that the first n variables on the righthand
side of the model are simple linear regressors (covariates)
and not factors in the quadratic response surface.
By default, PROC RSREG forms quadratic and crossproduct
effects for all regressor variables in the MODEL statement.
See the "Handling Covariates" section for more details and
Example 56.2 for an example using covariates.
 D

specifies that Cook's D influence statistic be written to the
output data set.
See Chapter 3, "Introduction to Regression Procedures," for details and formulas.
 LACKFIT

performs a lackoffit test.
Refer to Draper and Smith (1981) for a discussion of lackoffit tests.
 L95

specifies that the lower bound of a 95% confidence
interval for an individual predicted value be written to the output
data set.
The variance used in calculating this bound is a function of both
the mean square error and the variance of the parameter estimates.
See Chapter 3 for details and formulas.
 L95M

specifies that the lower bound of a 95% confidence interval
for the expected value of the dependent variable be written
to the output data set.
The variance used in calculating this bound is a
function of the variance of the parameter estimates.
See Chapter 3 for details and formulas.
 NOANOVA
 NOAOV

suppresses the display of the analysis of variance
and parameter estimates from the model fit.
 NOCODE

performs the canonical and ridge analyses with the
parameter estimates derived from fitting the response to
the original values of the factors variables, rather than
their coded values (see the "Coding the Factor Variables" section for more details.)
Use this option if the data are already stored in a coded
form.
 NOOPTIMAL
 NOOPT

suppresses the display of the canonical analysis
for the quadratic response surface.
 NOPRINT

suppresses the display of both the analysis
of variance and the canonical analysis.
 PREDICT

specifies that the values predicted by the model be written
to the output data set.
 PRESS

computes and displays the predicted residual sum of squares
(PRESS) statistic for each dependent variable in the model.
The PRESS statistic is added to the summary information
at the beginning of the analysis of variance, so if the
NOANOVA or NOPRINT option is specified, PRESS has no effect.
See Chapter 3 for details and formulas.
 RESIDUAL

specifies that the residuals, calculated as ACTUAL  PREDICTED,
be written to the output data set.
 U95

specifies that the upper bound of a 95% confidence
interval for an individual predicted value be written to the output
data set.
The variance used in calculating this bound is a function of both
the mean square error and the variance of the parameter estimates.
See Chapter 3 for details and formulas.
 U95M

specifies that the upper bound of a 95% confidence interval
for the expected value of the dependent variable be written to
the output data set.
The variance used in calculating this bound is a
function of the variance of the parameter estimates.
See Chapter 3 for details and formulas.
Copyright © 1999 by SAS Institute Inc., Cary, NC, USA. All rights reserved.