Scripting Guide - SAS

92 JSL Building Blocks Chapter 5
Compare Incomplete or Mismatched Data
Suppose that your data table shows the discount percentage for purchases of $25, $50, $75, and $100. You
want to create a graph that shows the discount for a $35 purchase, which the data table does not specify.
The following example shows the value that you want to evaluate, 35, followed by matrices for purchases
from $25 to $100.
Step(35, [25 50 75 100], [5 10 15 25]);
returns:
5
If the discounts were on a sliding scale (in this example, between 5 and 10, you would use Interpolate():
Interpolate(35, [25 50 75 100], [5 10 15 25]);
returns:
7
As with Interpolate(), the data points must create a positive slope.
Compare Incomplete or Mismatched Data
Comparing data that contains missing values can return misleading results unless you specify a condition
that is always true or use functions such as IsMissing() or ZeroOrMissing(). Comparisons of data with
mismatched types (numeric versus character) or data in matrices can also be confusing.
Table 5.4 shows examples of such comparisons and matrices and explanations of the results. For a review of
operators used in comparisons, see “Operators” on page 75. The sections that follow the table provide more
details about comparison and logical operators.
Note: Matrices must include the same number of columns or rows.
Table 5.4 Some Special-Case Comparison Tests
Test Result Explanation
m=.; m==1 . An equality test with a missing value returns missing.
m=.; m!=1 . An inequality test with a missing value returns missing.
m=.; m1; and so
on
. A comparison with a missing value returns missing (unless it
could not possibly be true, see next).
m=.; 1