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.
Follow @RickWicklin on Twitter.
How to vectorize computations in a matrix language
Last week someone posted an interesting question to the SAS/IML Support Community. The problem involved four nested DO loops and took hours to run. By transforming several nested DO loops into an equivalent matrix operation, I was able to reduce the run time to about one second. The process of [...]
Post a Comment Oh, those pesky temporary variables!
The SAS/IML language secretly creates temporary variables. Most of the time programmers aren’t even aware that the language does this. However, there is one situation where if you don’t think carefully about temporary variables, your program will silently produce an error. And as every programmer knows, silent wrong numbers are [...]
Post a Comment Generate binary outcomes with varying probability
A while ago I saw a blog post on how to simulate Bernoulli outcomes when the probability of generating a 1 (success) varies from observation to observation. I’ve done this often in SAS, both in the DATA step and in the SAS/IML language. For example, when simulating data that satisfied [...]
Post a Comment Remove or keep: Which is faster?
In a recent article on efficient simulation from a truncated distribution, I wrote some SAS/IML code that used the LOC function to find and exclude observations that satisfy some criterion. Some readers came up with an alternative algorithm that uses the REMOVE function instead of subscripts. I remarked in a [...]
Post a Comment Efficient acceptance-rejection simulation: Part II
Last week I wrote about using acceptance-rejection algorithms in vector languages to simulate data. The main point I made is that in a vector language it is efficient to generate many more variates than are needed, with the knowledge that a certain proportion will be rejected. In last week’s article, [...]
Post a Comment Efficient acceptance-rejection simulation
A few days ago on the SAS/IML Support Community, there was an interesting discussion about how to simulate data from a truncated Poisson distribution. The SAS/IML user wanted to generate values from a Poisson distribution, but discard any zeros that are generated. This kind of simulation is known as an [...]
Post a Comment Constructing block matrices with applications to mixed models
The other day I was constructing covariance matrices for simulating data for a mixed model with repeated measurements. I was using the SAS/IML BLOCK function to build up the “R-side” covariance matrix from smaller blocks. The matrix I was constructing was block-diagonal and looked like this: The matrix represents a [...]
Post a Comment Using macro loops for simulation
Last week I wrote an article in which I pointed out that many SAS programmers write a simulation in SAS by writing a macro loop. This approach is extremely inefficient, so I presented a more efficient technique. Not only is the macro loop approach slow, but there are other undesirable [...]
Post a Comment Simulation in SAS: The slow way or the BY way
Over the past few years, and especially since I posted my article on eight tips to make your simulation run faster, I have received many emails (often with attached SAS programs) from SAS users who ask for advice about how to speed up their simulation code. For this reason, I [...]
Post a Comment Indexing a SAS data set to improve processing categories in SAS/IML
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 [...]
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