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Rick Wicklin, PhD, is a distinguished researcher in computational statistics at SAS and is a principal developer of PROC IML and SAS/IML Studio. His areas of expertise include computational statistics, statistical graphics, statistical simulation, and modern methods in statistical data analysis. Rick is author of the books Statistical Programming with SAS/IML Software and Simulating Data with SAS.
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The SAS/IML language is used for many kinds of computations, but three important numerical tasks are integration, optimization, and root finding. Recently a SAS customer asked for help with a problem that involved all three tasks. The customer had an objective function that was defined in terms of an integral. […]Post a Comment
Bootstrap methods and permutation tests are popular and powerful nonparametric methods for testing hypotheses and approximating the sampling distribution of a statistic. I have described a SAS/IML implementation of a bootstrap permutation test for matched pairs of data (an alternative to a matched-pair t test) in my paper "Modern Data […]Post a Comment
Last week, as part of an article on how spammers generate comments for blogs, I showed how to generate random messages by using the CATX function in the DATA step. In that example, the strings were scalar quantities, but you can also concatenate vectors of strings in the SAS/IML language. […]Post a Comment
Dear Rick, I am trying to create a numerical matrix with 100,000 rows and columns in PROC IML. I get the following error: (execution) Unable to allocate sufficient memory. Can IML allocate a matrix of this size? What is wrong? Several times a month I see a variation of this […]Post a Comment
My previous post described how to use the "missing response trick" to score a regression model. As I said in that article, there are other ways to score a regression model. This article describes using the SCORE procedure, a SCORE statement, the relatively new PLM procedure, and the CODE statement. […]Post a Comment
A fundamental operation in statistical data analysis is to fit a statistical regression model on one set of data and then evaluate the model on another set of data. The act of evaluating the model on the second set of data is called scoring. One of first "tricks" that I […]Post a Comment
Vector languages such as SAS/IML, MATLAB, and R are powerful because they enable you to use high-level matrix operations (matrix multiplication, dot products, etc) rather than loops that perform scalar operations. In general, vectorized programs are more efficient (and therefore run faster) than programs that contain loops. For an example […]Post a Comment
If you write an n x p matrix from PROC IML to a SAS data set, you'll get a data set with n rows and p columns. For some applications, it is more convenient to write the matrix in a "long format" with np observations and three columns. The first […]Post a Comment
I was looking at someone else's SAS/IML program when I saw this line of code: y = sqrt(x<>0); The statement uses the element maximum operator (<>) in the SAS/IML language to make sure that negative value are never passed to the square root function. This little trick is a real […]Post a Comment
A challenge for statistical programmers is getting data into the right form for analysis. For graphing or analyzing data, sometimes the "wide format" (each subject is represented by one row and many variables) is required, but other times the "long format" (observations for each subject span multiple rows) is more […]Post a Comment