This is Part 4 of my response to Charlie Huang's interesting article titled Top 10 most powerful functions for PROC SQL. As I did for eaerlier topics, I will examine one of the "powerful" SQL functions that Charlie mentions and show how to do the same computation in SAS/IML software.
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
A colleague asked, "How can I enumerate the levels of a categorical classification variable in SAS/IML software?" The variable was a character variable with n observations, but he wanted the following: A "look-up table" that contains the k (unique) levels of the variable. A vector with n elements that contains
Over at the SAS/IML Discussion Forum, there have been several posts about how to call a Base SAS functions from SAS/IML when the Base SAS function supports a variable number of arguments. It is easy to call a Base SAS function from SAS/IML software when the syntax for the function
Andrew Ratcliffe posted a fine article titled "Inadequate Mends" in which he extols the benefits of including the name of a macro on the %MEND statement. That is, if you create a macro function named foo, he recommends that you include the name in two places: %macro foo(x); /** define
A fundamental operation in data analysis is finding data that satisfy some criterion. How many people are older than 85? What are the phone numbers of the voters who are registered Democrats? These questions are examples of locating data with certain properties or characteristics. The SAS DATA step has a
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
In a previous blog post, I showed how you can use simulation to construct confidence intervals for ranks. This idea (from a paper by E. Marshall and D. Spiegelhalter), enables you to display a graph that compares the performance of several institutions, where "institutions" can mean schools, companies, airlines, or
I recently returned from a five-day conference in Las Vegas. On the way there, I finally had time to read a classic statistical paper: Bayer and Diaconis (1992) describes how many shuffles are needed to randomize a deck of cards. Their famous result that it takes seven shuffles to randomize
