Different methods are available for dealing with missing values in your data. You may choose to use functionality available in Data Refinery or in SPSS Modeler nodes.
Functions available in SPSS Modeler
In SPSS Modeler, there are several functions used to handle missing values. The following functions are often used in Select and Filler nodes to discard or fill missing values:
count_nulls(LIST@BLANK(FIELD@NULL(FIELDundef
You can use @ functions in conjunction with the @FIELD function
to identify the presence of blank or null values in one or more fields. Simply flag the fields when
blank or null values are present, or fill them with replacement values or use them in a variety of
other operations.
You can count nulls across a list of fields, as follows:
count_nulls(['cardtenure' 'card2tenure' 'card3tenure'])When using any of the functions that accept a list of fields as input, you can use the special
functions @FIELDS_BETWEEN and @FIELDS_MATCHING, as shown in the
following example:
count_nulls(@FIELDS_MATCHING('card*'))You can use the undef function to fill fields with the system-missing value,
displayed as $null$. For example, to replace any numeric value, you can use a
conditional statement, such as:
if not(Age > 17) or not(Age < 66) then undef else Age endifThis replaces anything that isn't in the range with a system-missing value, displayed as
$null$. By using the not() function, you can catch all other
numeric values, including any negatives.
and not in the expression. For example, to select and include
all records where the type of prescription drug is Drug C, you would use the
following select statement:Drug = 'drugC' and not(@NULL(Drug))