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.
The new book on my desk
In my constant effort to keep pace with Chris Hemedinger, I am pleased to announce the availability of my new book, Simulating Data with SAS. Chris started a tradition for SAS Press authors to post a photo of themselves with their new book. Thanks to everyone who helped with the [...]
Post a Comment How to generate multiple samples from the multivariate normal distribution in SAS
A SAS customer asks: How do I use SAS to generate multiple samples of size N from a multivariate normal distribution? Suppose that you want to simulate k samples (each with N observations) from a multivariate normal distribution with a given mean vector and covariance matrix. Because all of the [...]
Post a Comment What happens if you misspecify the parameters for the "Table" distribution?
I have previously written about how to use the “table” distribution to generate random values from a discrete probability distribution. For example, if there are 50 black marbles, 20 red marbles, and 30 white marbles in a box, the following SAS/IML program simulates random draws (with replacement) of 1,000 marbles: [...]
Post a Comment Simulate discrete variables by using the "Table" distribution
I wanted to write a blog post about the “Table distribution” in SAS. The Table distribution, which is supported by the RAND and the RANDGEN function, enables you to specify the probability of selecting each of k items. Therefore you can use the Table distribution to sample, with replacement, from [...]
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 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 common covariance structures
I recently encountered a SUGI30 paper by Chuck Kincaid entitled “Guidelines for Selecting the Covariance Structure in Mixed Model Analysis.” I think Kincaid does a good job of describing some common covariance structures that are used in mixed models. One of the many uses for SAS/IML is as a language [...]
Post a Comment That distribution is quite PERT!
There are a lot of useful probability distributions that are not featured in standard statistical textbooks. Some of them have distinctive names. In the past year I have had contact with SAS customers who use the Tweedie distribution, the slash distribution, and the PERT distribution. Often these distributions are used [...]
Post a Comment Generate uniform data in a simplex
It is easy to simulate data that is uniformly distributed in the unit cube for any dimension. However, it is less obvious how to generate data in the unit simplex. The simplex is the set of points (x1,x2,…,xd) such that Σi xi = 1 and 0 ≤ xi ≤ 1 [...]
Post a Comment