MISSING Subcommand (REGRESSION command)
MISSING
controls
the treatment of cases with missing values. By default, a case that
has a user-missing or system-missing value for any variable named
or implied on VARIABLES
is omitted
from the computation of the correlation matrix on which all analyses
are based.
- The minimum specification is a keyword specifying a missing-value treatment.
LISTWISE. Delete cases
with missing values listwise. Only cases with valid values
for all variables named on the current VARIABLES
subcommand are used. If INCLUDE
is also specified, only cases with system-missing values are deleted
listwise. LISTWISE
is the default
if the MISSING
subcommand is
omitted.
PAIRWISE. Delete cases
with missing values pairwise. Each correlation coefficient
is computed using cases with complete data for the pair of variables
correlated. If INCLUDE
is also
specified, only cases with system-missing values are deleted pairwise.
MEANSUBSTITUTION. Replace missing
values with the variable mean. All cases are included
and the substitutions are treated as valid observations. If INCLUDE
is also specified, user-missing
values are treated as valid and are included in the computation of
the means.
INCLUDE. Includes cases
with user-missing values. All user-missing values are
treated as valid values. This keyword can be specified along with
the methods LISTWISE
, PAIRWISE
, or MEANSUBSTITUTION
.
Example
REGRESSION VARIABLES=POP15,POP75,INCOME,GROWTH,SAVINGS
/DEPENDENT=SAVINGS
/METHOD=STEP
/MISSING=MEANSUBSTITUTION.
- System-missing and user-missing values are replaced with the means of the variables when the correlation matrix is calculated.