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.
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
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
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
When you are working with probability distributions (normal, Poisson, exponential, and so forth), there are four essential functions that a statistical programmer needs. As I've written before, for common univariate distributions, SAS provides the following functions: the PDF function, which returns the probability density at a given point the CDF
Suppose that you have two data vectors, x and y, with the same number of elements. How can you rearrange the values of y so that they have the same relative order as the values of x? In other words, find a permutation, π, of the elements of y so
I've been working on a new book about Simulating Data with SAS. In researching the chapter on simulation of multivariate data, I've noticed that the probability density function (PDF) of multivariate distributions is often specified in a matrix form. Consequently, the multivariate density can usually be computed by using the
When I need to graph a function of two variables, I often choose to use a contour plot. A surface plot is probably easier for many people to understand, but it has several disadvantages when compared to a contour plot. For example, the following statements in SAS/IML Studio displays a
I received the following query regarding the RAND function in Base SAS: In SAS, is specifying 0 as a random number seed the same as not specifying a seed at all? The question concerns initializing the SAS random number stream by using the internal system clock. You can do this
I often use the SAS/IML language for simulating data with certain known properties. In fact, I'm writing a book called Simulating Data with SAS. When I simulate repeated measurements (sometimes called replicated data), I often want to generate an ID variable that identifies which measurement is associated with which subject
No matter what statistical programming language you use, be careful of testing for an exact value of a floating-point number. This is known in the world of numerical analysis as "10.0 times 0.1 is hardly ever 1.0" (Kernighan and Plauger, 1974, The Elements of Programming Style). There are many examples
