METHOD Subcommand (LOGISTIC REGRESSION command)
METHOD
indicates
how the independent variables enter the model. The specification is
the METHOD
subcommand followed
by a single method keyword. The keyword METHOD
can be omitted. Optionally, specify the independent variables and
interactions for which the method is to be used. Use the keyword BY
between variable names of an interaction
term.
- If no variable list is specified, or if the keyword
ALL
is used, all of the independent variables following the keywordWITH
on theVARIABLES
subcommand are eligible for inclusion in the model. - If no
METHOD
subcommand is specified, the default method isENTER
. - Variables that are specified on
CATEGORICAL
are replaced by sets of contrast variables. The set of contrast variables associated with a categorical variable is entered or removed from the model as a single step. - Any number of
METHOD
subcommands can appear in a Logistic Regression procedure.METHOD
subcommands are processed in the order in which they are specified. Each method starts with the results from the previous method. IfBSTEP
is used, all remaining eligible variables are entered at the first step. All variables are then eligible for entry and removal unless they have been excluded from theMETHOD
variable list. - The beginning model for the first
METHOD
subcommand is either the constant variable (by default or ifNOORIGIN
is specified) or an empty model (ifORIGIN
is specified).
The available METHOD
keywords are as follows:
ENTER. Forced entry. All variables are entered in a single step. This setting is the
default if the METHOD
subcommand
is omitted.
FSTEP. Forward stepwise. The variables (or interaction terms) that are specified on FSTEP
are tested for entry into the model
one by one, based on the significance level of the score statistic.
The variable with the smallest significance less than PIN
is entered into the model. After each
entry, variables that are already in the model are tested for possible
removal, based on the significance of the conditional statistic, the
Wald statistic, or the likelihood-ratio criterion. The variable with
the largest probability greater than the specified POUT
value is removed, and the model is
reestimated. Variables in the model are then evaluated again for removal.
When no more variables satisfy the removal criterion, covariates that
are not in the model are evaluated for entry. Model building stops
when no more variables meet entry or removal criteria or when the
current model is the same as a previous model.
BSTEP. Backward stepwise. As a first step, the variables (or interaction terms) that are specified
on BSTEP
are entered into the
model together and are tested for removal one by one. Stepwise removal
and entry then follow the same process as described for FSTEP
until no more variables meet entry
or removal criteria or when the current model is the same as a previous
model.
The statistic that is used in the test for removal
can be specified by an additional keyword in parentheses following FSTEP
or BSTEP
. If FSTEP
or BSTEP
is specified by itself, the default
is COND
.
COND. Conditional
statistic. This setting is the default if FSTEP
or BSTEP
is specified by itself.
WALD. Wald statistic. The removal of a variable from the model is based on the significance of the Wald statistic.
LR. Likelihood
ratio. The removal of a variable from the model is based
on the significance of the change in the log-likelihood. If LR
is specified, the model must be reestimated
without each of the variables in the model. This process can substantially
increase computational time. However, the likelihood-ratio statistic
is the best criterion for deciding which variables are to be removed.
Example
LOGISTIC REGRESSION
VARIABLES = PROMOTED WITH AGE JOBTIME JOBRATE RACE SEX AGENCY
/CATEGORICAL RACE SEX AGENCY
/METHOD ENTER AGE JOBTIME
/METHOD BSTEP (LR) RACE SEX JOBRATE AGENCY.
- AGE, JOBTIME, JOBRATE, RACE, SEX, and AGENCY are specified as independent variables. RACE, SEX, and AGENCY are specified as categorical independent variables.
- The first
METHOD
subcommand enters AGE and JOBTIME into the model. - Variables in the model at the termination of the
first
METHOD
subcommand are included in the model at the beginning of the secondMETHOD
subcommand. - The second
METHOD
subcommand adds the variables RACE, SEX, JOBRATE, and AGENCY to the previous model. - Backward stepwise logistic regression analysis is
then done with only the variables on the
BSTEP
variable list tested for removal by using theLR
statistic. - The procedure continues until all variables from
the
BSTEP
variable list have been removed or the removal of a variable will not result in a decrease in the log-likelihood with a probability larger thanPOUT
.