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 PROC IML and SAS/IML Studio. 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.

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The area under a density estimate curve

Readers' comments indicate that my previous blog article about computing the area under an ROC curve was helpful. Great! There is another common application of numerical integration: finding the area under a density estimation curve. This article provides an overview of density estimation and computes an empirical cumulative density function.

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Overlaying two histograms in SAS

A reader commented to me that he wants to use the HISTOGRAM statement of the SGPLOT procedure to overlay two histograms on a single plot. He could do it, but unfortunately SAS was choosing a large bin width for one of the variables and a small bin width for the

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Pre-allocate arrays to improve efficiency

Recently Charlie Huang showed how to use the SAS/IML language to compute an exponentially weighted moving average of some financial data. In the commentary to his analysis, he said: I found that if a matrix or a vector is declared with specified size before the computation step, the program’s efficiency

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A statistical analysis of coupons

Each Sunday, my local paper has a starburst image on the front page that proclaims "Up to $169 in Coupons!" (The value changes from week to week.) One day I looked at the image and thought, "Does the paper hire someone to count the coupons? Is this claim a good

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Enumerating levels of a classification variable

A colleague asked, "How can I enumerate the levels of a categorical classification variable in SAS/IML software?" The variable was a character variable with n observations, but he wanted the following: A "look-up table" that contains the k (unique) levels of the variable. A vector with n elements that contains

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Blogging, programming, and Johari windows

My primary purpose in writing The DO Loop blog is to share what I know about statistical programming in general and about SAS programming in particular. But I also write the blog for various personal reasons, including the enjoyment of writing. The other day I encountered a concept on Ajay

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Use subscript reduction operators!

Writing efficient SAS/IML programs is very important. One aspect to efficient SAS/IML programming is to avoid unnecessary DO loops. In my book, Statistical Programming with SAS/IML Software, I wrote (p. 80): One way to avoid writing unnecessary loops is to take full advantage of the subscript reduction operators for matrices.

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The trapezoidal rule of integration

In a previous article I discussed the situation where you have a sequence of (x,y) points and you want to find the area under the curve that is defined by those points. I pointed out that usually you need to use statistical modeling before it makes sense to compute the

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Listing SAS/IML variables

Did you know that you can display a list of all the SAS/IML variables (matrices) that are defined in the current session? The SHOW statement performs this useful task. For example, the following statements define three matrices: proc iml; fruit = {"apple", "banana", "pear"}; k = 1:3; x = j(1E5,

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Inadequate finishes

Andrew Ratcliffe posted a fine article titled "Inadequate Mends" in which he extols the benefits of including the name of a macro on the %MEND statement. That is, if you create a macro function named foo, he recommends that you include the name in two places: %macro foo(x); /** define

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Finding data that satisfy a criterion

A fundamental operation in data analysis is finding data that satisfy some criterion. How many people are older than 85? What are the phone numbers of the voters who are registered Democrats? These questions are examples of locating data with certain properties or characteristics. The SAS DATA step has a

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Calling R from SAS/IML software

For years I've been making presentations about SAS/IML software at conferences. Since 2008, I've always mentioned to SAS customers that they can call R from within SAS/IML software. (This feature was introduced in SAS/IML Studio 3.2 and was added to the IML procedure in SAS/IML 9.22.) I also included a

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The COALESCE function: PROC SQL compared with PROC IML

When Charlie H. posted an interesting article titled "Top 10 most powerful functions for PROC SQL," there was one item on his list that was unfamiliar: the COALESCE function. (Edit: Charlie's blog no longer exists. The article used to be available at http://www.sasanalysis.com/2011/01/top-10-most-powerful-functions-for-proc.html) Ever since I posted my first response,

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Where do major airlines fly?

Last week the Flowing Data blog published an excellent visualization of the flight patterns of major US airlines. On Friday, I sent the link to Robert Allison, my partner in the 2009 ASA Data Expo, which explored airline data. Robert had written a SAS program for the Expo that plots

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How to numerically integrate a function in SAS

This blog post shows how to numerically integrate a one-dimensional function by using the QUAD subroutine in SAS/IML software. The name "quad" is short for quadrature, which means numerical integration. You can use the QUAD subroutine to numerically find the definite integral of a function on a finite, semi-infinite, or

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Variable transformations

One of the advantages of programming in the SAS/IML language is its ability to transform data vectors with a single statement. For example, in data analysis, the log and square-root functions are often used to transform data so that the transformed data have approximate normality. The following SAS/IML statements create

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An improved simulation of card shuffling

Last week I presented the GSR algorithm, a statistical model of a riffle shuffle. In the model, a deck of n cards is split into two parts according to the binomial distribution. Each piece has roughly n/2 cards. Then cards are dropped from the two stacks according to the number

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