About this blog
Rick Wicklin, PhD, is a senior 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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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
While walking in the woods, a statistician named Goldilocks wanders into a cottage and discovers three bears. The bears, being hungry, threaten to eat the young lady, but Goldilocks begs them to give her a chance to win her freedom. The bears agree. While Mama Bear and Papa Bear block [...]Post a Comment
Sometimes it is useful in the SAS/IML language to convert a character string into a vector of one-character values. For example, you might want to count the frequency distribution of characters, which is easy when each character is an element of a vector. The question of how to convert a [...]Post a Comment
Are you still using the old RANUNI, RANNOR, RANBIN, and other "RANXXX" functions to generate random numbers in SAS? If so, here are six reasons why you should switch from these older (1970s) algorithms to the newer (late 1990s) Mersenne-Twister algorithm, which is implemented in the RAND function. The newer [...]Post a Comment
A SAS user told me that he computed a vector of values in the SAS/IML language and wanted to use those values on a statement in a SAS procedure. The particular application involved wanting to use the values on the ESTIMATE and CONTRAST statements in a SAS regression procedure, but [...]Post a Comment