"Always clean up after yourself." My mother taught me this, and I apply it to SAS programming as regularly as I apply it at home. For SAS programming, I reinterpret Mom's saying as the following rule: Always delete temporary files and data sets when you are finished using them. How
In Base SAS you can use the DATASETS procedure to determine the SAS data sets in a library, and you can use the DELETE statement to delete data sets. Did you know that you can do the same operations from within the SAS/IML language? The following DATA step creates four
Recently Charlie Huang showed how to use the SAS/IML language to compute an exponentially weighted moving average of some financial data. In the commentary to his analysis, he said: I found that if a matrix or a vector is declared with specified size before the computation step, the program’s efficiency
Writing efficient SAS/IML programs is very important. One aspect to efficient SAS/IML programming is to avoid unnecessary DO loops. In my book, Statistical Programming with SAS/IML Software, I wrote (p. 80): One way to avoid writing unnecessary loops is to take full advantage of the subscript reduction operators for matrices.
Did you know that you can display a list of all the SAS/IML variables (matrices) that are defined in the current session? The SHOW statement performs this useful task. For example, the following statements define three matrices: proc iml; fruit = {"apple", "banana", "pear"}; k = 1:3; x = j(1E5,
When I was at the annual SAS Global Forum conference, I had the pleasure of discussing statistical programming and SAS/IML software with dozens of SAS customers. I was asked at least ten times, "How do I get started with SAS/IML software?" or "How can I learn PROC IML?" Here is
More than a month ago I wrote a first article in response to an interesting article by Charlie H. titled Top 10 most powerful functions for PROC SQL. In that article I described SAS/IML equivalents to the MONOTONIC, COUNT, N, FREQ, and NMISS Functions in PROC SQL. In this article,
The most common way to read observations from a SAS data set into SAS/IML matrices is to read all of the data at once by using the ALL clause in the READ statement. However, the READ statement also has options that do not require holding all of the observations in
One of the advantages of programming in the SAS/IML language is its ability to transform data vectors with a single statement. For example, in data analysis, the log and square-root functions are often used to transform data so that the transformed data have approximate normality. The following SAS/IML statements create
In a previous post, I showed how to read data from a SAS data set into SAS/IML matrices or vectors. This article shows the converse: how to use the CREATE, APPEND, and CLOSE statements to create a SAS data set from data stored in a matrix or in vectors. Creating
