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, and modern methods in statistical data analysis. Rick is author of the book Statistical Programming with SAS/IML Software.
Follow @RickWicklin on Twitter.
Extending SAS: How to define new functions in PROC FCMP and SAS/IML software
SAS software provides many run-time functions that you can call from your SAS/IML or DATA step programs. The SAS/IML language has several hundred built-in statistical functions, and Base SAS software contains hundreds more. However, it is common for statistical programmers to extend the run-time library to include special user-defined functions. [...]
Post a Comment Vectorized computations and the birthday matching problem
The birthday matching problem is a classic problem in probability theory. The part of it that people tend to remember is that in a room of 23 people, there is greater than 50% chance that two people in the room share a birthday. But the birthday matching problem is also [...]
Post a Comment Defining banded and triangular matrices: Another approach
Sometimes in matrix computations you need to obtain the values of certain submatrices such as the diagonal elements or the super- or subdiagonal elements. About a year ago, I showed one way to do that: convert subscripts to indices and vice-versa. However, a tip from @RLangTip on Twitter got me [...]
Post a Comment The DATA step and the implied AND operator
The SAS DATA step supports a special syntax for determining whether a value is contained in an interval: y = (-2 < x < 2); This expression creates an indicator variable with the value 1 if x is in the interval (-2,2) and 0 otherwise. The documentation for the AND [...]
Post a Comment Convert a covariance matrix to a correlation matrix in SAS
I have previously blogged about how to convert a covariance matrix into a correlation matrix in SAS (and the other way around). However, I still get questions about it, perhaps because my previous post demonstrated more than one way to accomplish each transformation. To eliminate all confusion, the following SAS/IML [...]
Post a Comment Avoid unnecessary IF-THEN statements in loops
Way back when I learned to program, I remember a computer instructor explaining that an IF-THEN statement can be a relatively slow operation. He said “If a multiplication takes one unit of time, an IF statement requires about 70 units.” I don’t know where his numbers came from, or even [...]
Post a Comment Use the Cholesky transformation to correlate and uncorrelate variables
A variance-covariance matrix expresses linear relationships between variables. Given the covariances between variables, did you know that you can write down an invertible linear transformation that “uncorrelates” the variables? Conversely, you can transform a set of uncorrelated variables into variables with given covariances. The transformation that works this magic is [...]
Post a Comment How to access SAS sample programs
Have you ever wanted to run a sample program from the SAS documentation or wanted to use a data set that appears in the SAS documentation? You can: all programs and data sets in the documentation are distributed with SAS, you just have to know where to look! Sample data [...]
Post a Comment Constants in SAS
Statistical programmers often need mathematical constants such as π (3.14159…) and e (2.71828…). Programmers of numerical algorithms often need to know machine-specific constants such as the machine precision constant (2.22E-16 on my Windows PC) or the largest representable double-precision value (1.798E308 on my Windows PC). Some computer languages build these [...]
Post a Comment Compute a running mean and variance
In my recent article on simulating Buffon’s needle experiment, I computed the “running mean” of a series of values by using a single call to the CUSUM function in the SAS/IML language. For example, the following SAS/IML statements define a RunningMean function, generate 1,000 random normal values, and compute the [...]
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