About this blog
Rick Wicklin, PhD, is a distinguished 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.
Subscribe to this blog
Tags9.3 9.4 9.22 12.1 12.3 13.1 Bootstrap and Resampling Conferences Data Analysis Efficiency File Exchange Getting Started GTL History IMLPlus Just for Fun Matrix Computations Numerical Analysis Optimization R Reading and Writing Data Sampling and Simulation SAS/IML Studio SAS Programming Statistical Graphics Statistical Programming Statistical Thinking Strings Tips and Techniques vectorization Video
In a previous blog post, I described how to generate combinations in SAS by using the ALLCOMB function in SAS/IML software. The ALLCOMB function in Base SAS is the equivalent function for DATA step programmers. Recall that a combination is a unique arrangement of k elements chosen from a set […]Post a Comment
Have you written a SAS/IML program that you think is particularly clever? Are you the proud author of SAS/IML functions that extend the functionality of SAS software? You've worked hard to develop, debug, and test your program, so why not share it with others? There is now a central location […]Post a Comment
In my four years of blogging, the post that has generated the most comments is "How to handle negative values in log transformations." Many people have written to describe data that contain negative values and to ask for advice about how to log-transform the data. Today I describe a transformation […]Post a Comment
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. […]Post a Comment
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 […]Post a Comment
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 […]Post a Comment
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 […]Post a Comment
In my recent post on how to understand character vectors in SAS/IML, I left out an important topic: How can you allocate a character vector of a specified length? In this article, "length" means the maximum number of characters in an element, not the number of elements in a vector. […]Post a Comment
Last week Chris Hemedinger posted an article about spam that is sent to SAS blogs and discussed how anti-spam software helps to block spam. No algorithm can be 100% accurate at distinguishing spam from valid comments because of the inherent trade-off between specificity and sensitivity in any statistical test. Therefore, […]Post a Comment
SAS programmers are probably familiar with how SAS stores a character variable in a data set, but how is a character vector stored in the SAS/IML language? Recall that a character variable is stored by using a fixed-width storage structure. In the SAS DATA step, the maximum number of characters […]Post a Comment