Rick Wicklin, PhD, is a distinguished researcher in computational statistics at SAS and is a principal developer of SAS/IML software. His areas of expertise include computational statistics, simulation, statistical graphics, and modern methods in statistical data analysis. Rick is author of the books Statistical Programming with SAS/IML Software and Simulating Data with SAS.
When Charlie H. posted an interesting article titled "Top 10 most powerful functions for PROC SQL," there was one item on his list that was unfamiliar: the COALESCE function. (Edit: Charlie's blog no longer exists. The article used to be available at http://www.sasanalysis.com/2011/01/top-10-most-powerful-functions-for-proc.html) Ever since I posted my first response,
Last week the Flowing Data blog published an excellent visualization of the flight patterns of major US airlines. On Friday, I sent the link to Robert Allison, my partner in the 2009 ASA Data Expo, which explored airline data. Robert had written a SAS program for the Expo that plots
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
This blog post shows how to numerically integrate a one-dimensional function by using the QUAD subroutine in SAS/IML software. The name "quad" is short for quadrature, which means numerical integration. You can use the QUAD subroutine to numerically find the definite integral of a function on a finite, semi-infinite, or
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
In last week's article on how to create a funnel plot in SAS, I wrote the following comment: I have not adjusted the control limits for multiple comparisons. I am doing nine comparisons of individual means to the overall mean, but the limits are based on the assumption that I'm
The log transformation is one of the most useful transformations in data analysis. It is used as a transformation to normality and as a variance stabilizing transformation. A log transformation is often used as part of exploratory data analysis in order to visualize (and later model) data that ranges over
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
Last week I showed how to create a funnel plot in SAS. A funnel plot enables you to compare the mean values (or rates, or proportions) of many groups to some other value. The group means are often compared to the overall mean, but they could also be compared to
