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.
As you develop a program in the SAS/IML language, it is often useful to create graphs to visualize intermediate results. The language supports basic statistical graphics such as bar charts, histograms, scatter plots, and so on. However, you can create more advanced graphics without leaving PROC IML by using the
A colleague asked me an interesting question: I have a journal article that includes sample quantiles for a variable. Given a new data value, I want to approximate its quantile. I also want to simulate data from the distribution of the published data. Is that possible? This situation is common.
I've pointed out in the past that in the SAS/IML language matrices are passed to modules "by reference." This means that large matrices are not copied in and out of modules but are updated "in place." As a result, the SAS/IML language can be very efficient when it computes with
Today is my 500th blog post for The DO Loop. I decided to celebrate by doing what I always do: discuss a statistical problem and show how to solve it by writing a program in SAS. Two ways to parameterize the lognormal distribution I recently blogged about the relationship between
Nonlinear optimization routines enable you to find the values of variables that optimize an objective function of those variables. When you use a numerical optimization routine, you need to provide an initial guess, often called a "starting point" for the algorithm. Optimization routines iteratively improve the initial guess in an
In many areas of statistics, it is convenient to be able to easily construct a uniform grid of points. You can use a grid of parameter values to visualize functions and to get a rough feel for how an objective function in an optimization problem depends on the parameters. And
In my book Simulating Data with SAS, I specify how to generate lognormal data with a shape and scale parameter. The method is simple: you use the RAND function to generate X ~ N(μ, σ), then compute Y = exp(X). The random variable Y is lognormally distributed with parameters μ
Sometimes you have data in SAS/IML vectors that you need to write to a SAS data set. By default, no formats are associated with the variables that you create from SAS/IML vectors. However, some variables (notably dates, times, and datetimes) should have formats associated with the data values. You can
Bootstrap methods and permutation tests are popular and powerful nonparametric methods for testing hypotheses and approximating the sampling distribution of a statistic. I have described a SAS/IML implementation of a bootstrap permutation test for matched pairs of data (an alternative to a matched-pair t test) in my paper "Modern Data
A little-known but useful feature of SAS/IML 12.3 (which was released with SAS 9.4) is the ability to generate a vector of lowercase or uppercase letters by using the colon operator (:). Many SAS/IML programmers use the colon operator to generate a vector of sequential integers: proc iml; x =
