## Effect Selection Methods

Five effect-selection methods are available. The simplest method (and
the default)
is SELECTION=NONE, for which PROC LOGISTIC fits the complete model as specified
in the MODEL statement. The other four methods are FORWARD for
forward selection, BACKWARD for backward elimination, STEPWISE for
stepwise selection, and SCORE for best subsets selection. These methods are
specified with the SELECTION= option in the MODEL statement. Intercept
parameters are forced to stay in the model unless the NOINT option
is specified.
When SELECTION=FORWARD, PROC LOGISTIC first estimates parameters for
effects forced into the model. These effects are the intercepts
and the first *n* explanatory effects in the MODEL statement, where
*n* is the number specified by the START= or INCLUDE= option in the
MODEL statement (*n* is zero by default). Next, the procedure
computes the score chi-square statistic for each effect not in
the model and examines the largest of these statistics. If it is
significant at the SLENTRY= level, the corresponding effect is added to the
model. Once an effect is entered in the model, it is never removed from the
model. The process is repeated until none of the remaining effects
meet the specified level for entry or until the STOP= value is reached.

When SELECTION=BACKWARD, parameters for the complete model as
specified in the MODEL statement are estimated unless the START= option is
specified. In that case, only the parameters for the intercepts and
the first *n* explanatory effects in the MODEL statement are
estimated, where *n* is the number specified by the START= option.
Results of the Wald test for individual parameters are examined.
The least significant effect that does not meet the SLSTAY= level
for staying in the model is removed. Once an effect is removed
from the model, it remains excluded. The process is repeated until
no other effect in the model meets the specified level for
removal or until the STOP= value is reached. Backward selection
is often less successful than forward or stepwise selection because the
full model fit in the first step is the model most likely to result in a
complete or quasi-complete separation of response values as described in
the previous section.

The SELECTION=STEPWISE option is similar to the SELECTION=FORWARD
option except that effects already in the model do not necessarily
remain. Effects are entered into and removed from
the model in such a way that each forward selection step may be
followed by one or more backward elimination
steps. The stepwise selection process terminates if no
further effect can be added to the model or if the effect
just entered into the model is the only effect removed in the subsequent
backward elimination.

For SELECTION=SCORE,
PROC LOGISTIC uses the branch
and bound algorithm
of Furnival and Wilson (1974) to find a
specified number of models with the highest likelihood score
(chi-square) statistic for all possible model sizes,
from 1, 2, 3 effect models, and so on, up to the single model
containing all of the explanatory effects. The number of models
displayed for each model size is controlled by the BEST= option.
You can use the START= option to impose a minimum model size, and you
can use the STOP= option to impose a maximum model size. For instance,
with BEST=3, START=2, and STOP=5, the SCORE selection method displays
the best three models (that is, the three models with the highest score
chi-squares) containing 2, 3, 4, and 5 effects. The SELECTION=SCORE
option is not available for models with CLASS variables.

The options FAST, SEQUENTIAL, and STOPRES can alter the default
criteria for entering or removing effects from the model when
they are used with the FORWARD, BACKWARD, or STEPWISE selection
methods.

Copyright © 1999 by SAS Institute Inc., Cary, NC, USA. All rights reserved.