Scripting Guide - SAS

Chapter 5 JSL Building Blocks 91
Conditional Functions
Interpolate
Step
The Interpolate() function finds the y value corresponding to a given x value between two points (x1,
y1 and x2, y2). A linear interpolation is applied to the values. You might use Interpolate() to calculate
missing values between data points.
The data points can be specified as a list:
Interpolate(x, x1, y1, x2, y2, ...)
or as matrices containing the x and y values:
Interpolate(x, xmatrix, ymatrix)
Suppose that your data set includes the height of individuals from age 20 through 25. However, there is no
data for age 23. To estimate the height for 23-year-olds, use interpolation. The following example shows the
value that you want to evaluate (age 23), followed by matrices for ages (20 through 25) and heights (59
through 75).
Interpolate(23, [20 21 22 24 25], [59 62 56 69 75]);
returns:
62.5
The value 62.5 is halfway between the y values 56 and 69, just as 23 is halfway between the x values 22
and 24.
Notes:
• The data points in each list or matrix must create a positive slope. For example,
Interpolate(2,1,1,3,3) returns 2. However, Interpolate(2,3,3,1,1) returns a missing value
(.).
• Interpolate is best used for continuous data, but Step() is for discrete data. See “Step” on page 91
for details.
The Step() is like Interpolate() except that it finds the corresponding y for a given x from a
step-function fit rather than a linear fit. Use Step() with discrete y values (that is, when the y value’s
corresponding x value can be only y 1 or y 2 ). However, when the y value’s corresponding x value can fall
between y 1 and y 2 , use Interpolate().
As with Interpolate, the data points can be specified as a list:
Step(x, x1, y1, x2, y2, ...)
or as matrices containing the x and y values:
Step(x, xmatrix, ymatrix)