Author

Rick Wicklin
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Distinguished Researcher in Computational Statistics

Rick Wicklin, PhD, is a distinguished researcher in computational statistics at SAS and is a principal developer of SAS/IML software. His areas of expertise include computational statistics, simulation, statistical graphics, and modern methods in statistical data analysis. Rick is author of the books Statistical Programming with SAS/IML Software and Simulating Data with SAS.

Learn SAS
Rick Wicklin 0
How the J function got its name

In linear algebra, the I symbol is used to denote an n x n identity matrix. The symbol J (or sometimes 1) is used to denote an n x p matrix of ones. When the SAS/IML language was implemented, the I function was defined to generate the identity matrix. The J function was defined

Learn SAS
Rick Wicklin 0
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

Rick Wicklin 0
Reading special SAS data sets into matrices

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

Rick Wicklin 0
A fractal Christmas tree in SAS

In my previous post, I described how to implement an iterated function system (IFS) in the SAS/IML language to draw fractals. I used the famous Barnsley fern example to illustrate the technique. At the end of the article I issued a challenge: can you construct an IFS whose fractal attractor

Rick Wicklin 0
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

Learn SAS
Rick Wicklin 0
Beware the naked LOC

The LOC function is one of the most important functions in the SAS/IML language. The LOC function finds elements of a vector or matrix that satisfy some condition. For example, if you are going to apply a logarithmic transform to data, you can use the LOC function to find all

Rick Wicklin 0
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

Rick Wicklin 0
Inverse hyperbolic functions in SAS

I was recently asked, "Does SAS support computing inverse hyperbolic trigonometric functions?" I was pretty sure that I had used the inverse hyperbolic trig functions in SAS, so I was surprised when I read the next sentence: "I ask because I saw a Usage Note that says these functions are

Rick Wicklin 0
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

Advanced Analytics
Rick Wicklin 0
Compute the log-determinant of a matrix

The determinant of a matrix arises in many statistical computations, such as in estimating parameters that fit a distribution to multivariate data. For example, if you are using a log-likelihood function to fit a multivariate normal distribution, the formula for the log-likelihood involves the expression log(det(Σ)), where Σ is the

Rick Wicklin 0
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

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