Specifies the maximum number of steps for the STEPWISE variable selection method.
Two times the number of effects specified in the MODEL statement.
Suppresses the display of the coding of the CLASS inputs.
Specifies the rule for inclusion of effects for SELECTION=FORWARD, BACKWARD, or
hierarchical rule is observed. For example, if main effects A and B and interactions A*B are
not in the model, effects that can be considered for entry in a single step are A alone, or B
alone, or A, B, and A*B together.
A single effect is considered for entry into the model only if its lower order effects are
higher order effects are not in the model.
Effects are included or excluded one at a time without preservation of any hierarchical
For more complete definitions of the selection methods, see pages 1397-1398
of the "SAS/STAT User's Guide, Volume 2, GLM-VARCOMP, Version 6
Specifies the variable selection methods.
Begins with no inputs in the model and then, systematically, adds inputs that are related to
related to the target.
Systematically adds and deletes inputs from the model. Stepwise selection is similar to
forward selection except that stepwise may remove an input after it has entered the model
Specifies the addition or deletion of variables in sequential order, as specified in the MODEL
Specifies the significance level for addition of variables.
Specifies the significance level for removal of variables.
Specifies that the first n effects be included in the starting model.
0 - for the FORWARD or the STEPWISE method
s (the total number of effects in the MODEL statement)- for the
The value of n ranges from 0 to s, where s is the total number of effects in the
Specifies the maximum (FORWARD method) or minimum (BACKWARD method) number of
effects to be included in the final model. The variable selection process is stopped when n effects
are added or deleted. The STOP= option has no effect when SELECTION=NONE or STEPWISE.
s - for the FORWARD method
0 - for the BACKWARD method
MODEL Options - Specification Options
Specifies design variable coding for CLASS inputs.
Deviation from mean coding, which is also known as effect coding.
Non-full rank GLM coding as usedin the GLM procedure.
Specifies the measurement level of the target variable.
ORDINAL for a categorical target; INTERVAL for a numerical target.
Specifies the error distribution.
Multinomial distribution with on trial. This includes the binomial distribution with on trial.
MBERNOULLI is not available if the target meausurement level is interval.
BINOMAIL or MULTINOMIAL
Normal distribution. NORMAL is not allowed e if the target measurement level is nominal.
ERROR=NORMAL (for LEVEL=INTERVAL), ERROR=MBERNOULLI
LINK= CLOGLOG | IDENTITY | LOGIT | PROBIT
Specifies the link function that represents the expected values of the target to the linear predictors.
Specifies the complementary log-log function, which is the inverse of the extreme value
Specifies the identity function. The IDENTITY function can only be used for the linear
LOGIT function is available for nominal, ordinal, or binary targets.
Specifies the probit function, which is the inverse of the standard normal distribution
LOGIT (for ERROR=MBERNOULLI), IDENTITY (for
IDENTITY (for ERROR=NORMAL)
The CLOGLOG, LOGIT, and PROBIT link functions are used for a logistic
regression analysis. The IDENTITY link function is used for a linear
Suppresses the intercept for the binary target model or the normal error linear regression model.
Specifies the tolerance for testing singularity.
Copyright 2000 by SAS Institute Inc., Cary, NC, USA. All rights reserved.