MAXSTEP=n
Specifies the maximum number of steps for the STEPWISE variable selection method.
Default:
Two times the number of effects specified in the MODEL statement.
NODESIGNPRINT
Suppresses the display of the coding of the CLASS inputs.
ALIAS:
NODP
RULE=MULTIPLE | SINGLE | NONE
Specifies the rule for inclusion of effects for SELECTION=FORWARD, BACKWARD, or
STEPWISE.
MULTIPLE
One or more effects can be considered for entry or removal at the same time provided the
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.
SINGLE
A single effect is considered for entry into the model only if its lower order effects are
already in the model; a single effect is considered for removal from the model only if its
higher order effects are not in the model.
NONE
Effects are included or excluded one at a time without preservation of any hierarchical
order.
Default:
RULE=NONE
Reference:
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
Edition."
SELECTION= FORWARD | BACKWARD | STEPWISE | NONE
Specifies the variable selection methods.
FORWARD
Begins with no inputs in the model and then, systematically, adds inputs that are related to
the target.
BACKWARD
Begins with all inputs in the model and then, systematically, removes inputs that are not
related to the target.
STEPWISE
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
and replace it with another input.
NONE
All inputs are used to fit the model.
Default:
NONE
SEQUENTIAL
Specifies the addition or deletion of variables in sequential order, as specified in the MODEL
statement.
SLENTRY=n
Specifies the significance level for addition of variables.
Default:
.05
SLSTAY=n
Specifies the significance level for removal of variables.
Default:
.05
START=n
Specifies that the first n effects be included in the starting model.
Default:
0 - for the FORWARD or the STEPWISE method
s (the total number of effects in the MODEL statement)- for the
BACKWARD method
Range:
The value of n ranges from 0 to s, where s is the total number of effects in the
MODEL statement.
STOP=n
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.
Range:
The value of n ranges from 0 to s, where s is the total number of effects in the
MODEL statement.
Default:
s - for the FORWARD method
0 - for the BACKWARD method
MODEL Options - Specification Options
CODING= DEVIATION | GLM
Specifies design variable coding for CLASS inputs.
DEVIATION
Deviation from mean coding, which is also known as effect coding.
GLM
Non-full rank GLM coding as usedin the GLM procedure.
Default:
CODING=DEVIATION
LEVEL=INTERVAL | NOMINAL | ORDINAL
Specifies the measurement level of the target variable.
INTERVAL
Interval variable.
NOMINAL
Nominal variable.
ORDINAL
Ordinal variable.
Default:
ORDINAL for a categorical target; INTERVAL for a numerical target.
ERROR=MBERNOULLI | NORMAL
Specifies the error distribution.
MBERNOULLI
Multinomial distribution with on trial. This includes the binomial distribution with on trial.
MBERNOULLI is not available if the target meausurement level is interval.
Alias:
BINOMAIL or MULTINOMIAL
NORMAL
Normal distribution. NORMAL is not allowed e if the target measurement level is nominal.
Default:
ERROR=NORMAL (for LEVEL=INTERVAL), ERROR=MBERNOULLI
(otherwise).
LINK= CLOGLOG | IDENTITY | LOGIT | PROBIT
Specifies the link function that represents the expected values of the target to the linear predictors.
CLOGLOG
Specifies the complementary log-log function, which is the inverse of the extreme value
distribution function. The CLOGLOG function is available is available for ordinal or binary
targets.
IDENTITY
Specifies the identity function. The IDENTITY function can only be used for the linear
regression analysis (ERROR=NORMAL).
LOGIT
Specifies the logit function, which is the inverse of the logistic distribution function. The
LOGIT function is available for nominal, ordinal, or binary targets.
PROBIT
Specifies the probit function, which is the inverse of the standard normal distribution
function. The PROBIT function is available is available for ordinal or binary targets.
Default:
LOGIT (for ERROR=MBERNOULLI), IDENTITY (for
ERROR=NORMAL).
IDENTITY (for ERROR=NORMAL)
Tip:
The CLOGLOG, LOGIT, and PROBIT link functions are used for a logistic
regression analysis. The IDENTITY link function is used for a linear
regression analysis.
NOINT
Suppresses the intercept for the binary target model or the normal error linear regression model.
SINGULAR= n
Specifies the tolerance for testing singularity.
Default:
Copyright 2000 by SAS Institute Inc., Cary, NC, USA. All rights reserved.
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