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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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Tags9.3 9.4 9.22 12.1 12.3 13.1 13.2 Bootstrap and Resampling Ciphers Conferences Data Analysis Efficiency File Exchange Getting Started GTL Heat maps History IMLPlus Just for Fun Math Matrix Computations Numerical Analysis Optimization R Reading and Writing Data SAS/IML Studio SAS Programming Simulation Statistical Graphics Statistical Programming Statistical Thinking Strings Tips and Techniques vectorization Video
I often blog about the usefulness of vectorization in the SAS/IML language. A one-sentence summary of vectorization is "execute a small number of statements that each analyze a lot of data." In general, for matrix languages (SAS/IML, MATLAB, R, ...) vectorization is more efficient than the alternative, which is to […]Post a Comment
Many people know that the SAS/IML language enables you to read data from and write results to multiple SAS data sets. When you open a new data set, it is a good programming practice to close the previous data set. But did you know that you can have two data […]Post a Comment
Do you have dozens (or even hundreds) of SAS data sets that you want to read into SAS/IML matrices? In a previous blog post, I showed how to iterate over a series of data sets and analyze each one. Inside the loop, I read each data set into a matrix […]Post a Comment
One of my favorite features of SAS/IML 12.1 (released with 9.3m2) is that the USE and CLOSE statements support reading data set names that are specified in a SAS/IML matrix. The IMLPlus language in SAS/IML Studio has supported this syntax since the early 2000s, so I am pleased that this […]Post a Comment
SAS has several kinds of special data sets whose contents are organized according to certain conventions. These special data sets are marked with the TYPE= data set attribute. For example, the CORR procedure can create a data set with the TYPE=CORR attribute. You can decipher the structure of the data […]Post a Comment
A SAS/IML user on a discussion forum was trying to read data into a SAS/IML matrix, but the data was so large that it would not fit into memory. (Recall that SAS/IML matrices are kept in RAM.) After a few questions, it turned out that the user was trying to […]Post a Comment
Many SAS procedures can produce ODS statistical graphics as naturally as they produce tables. Did you know that it is possible to obtain the numbers underlying an ODS statistical graph? This post shows how. Suppose that a SAS procedure creates a graph that displays a curve and that you want […]Post a Comment
I have blogged about three different SAS/IML techniques that iterate over categories and process the observations in each category. The three techniques are as follows: Use a WHERE clause on the READ statement to read only the observations in the ith category. This is described in the article "BY-group processing […]Post a Comment
One of the first skills that a beginning SAS/IML programmer learns is how to read data from a SAS data set into SAS/IML vectors. (Alternatively, you can read data into a matrix). The beginner is sometimes confused about the syntax of the READ statement: do you specify the names of […]Post a Comment
Covariance, correlation, and distance matrices are a few examples of symmetric matrices that are frequently encountered in statistics. When you create a symmetric matrix, you only need to specify the lower triangular portion of the matrix. The VECH and SQRVECH functions, which were introduced in SAS/IML 9.3, are two functions […]Post a Comment