The SAS/IML language is a vector language, so statements that operate on a few long vectors run much faster than equivalent statements that involve many scalar quantities. For example, in a previous post, I asserted that the LOC function is much faster than writing a loop, for finding observations that
The Junk Chart blog discusses a potential problem that can arise in grouped bar charts when the two groups have vastly different ranges. One possible solution (which is discussed at the Junk Chart sister blog, Numbers Rule Your World) is to present the data back-back in what is sometimes called
The SAS/IML run-time library contains hundreds of functions and subroutines that you can call to perform statistical analysis. There are also many functions in Base SAS software that you can call from SAS/IML programs. However, one day you might need to compute some quantity for which there is no prewritten
Visualizing the distribution of data is a primary task of data analysis. With all the hurricane activity in the Atlantic this year, I’ve been thinking about ways to visualize the historical distribution of hurricane activity. USA Today on Friday, August 13, 2010, announced that "the heart of hurricane season is
Recently, SAS Global Forum announced the call for papers for the 2011 conference to be held at Caesars Palace in Las Vegas. Since the conference is in Las Vegas, I’ve been thinking a lot about games of chance: blackjack, craps, roulette, and the like. You can analyze these games by
My mother taught me to put things away when I'm finished using them. She doesn't use a computer, but if she did, I know that she'd approve of this tip from my book: Tip: Always close your files and data sets when you are finished reading or writing them. In
Today is the birthday of Bernhard Riemann, a German mathematician who made fundamental contributions to the fields of geometry, analysis, and number theory. Riemann is definitely on my list of the greatest mathematicians of all time, and his conjecture about the distribution of prime numbers is one of the great
A friend recently asked me why I am writing a book. My answer? Some people are born to write a book and some have books thrust upon them. Mine was thrust upon me, although it is more accurate to say that I thrust it upon myself. My book, Statistical Programming
Missing values are a fact of life. Many statistical analyses, such as regression, exclude observations that contain missing values prior to forming matrix equations that are used in the analysis. This post shows how to find rows of a data matrix that contain missing values and how to remove those
Peter Flom reminded readers of his blog that you should always end a SAS procedure with a RUN statement. This is good rule. However, PROC IML is an exception to the rule. In PROC IML, the RUN statement is used to execute a built-in subroutine or a user-defined module. You
A frequently performed task in data analysis is identifying all the observations in a data set that satisfy certain conditions. For example, you might want to identify all of the female patients in your study or to identify all patients whose systolic blood pressure is greater than 140 mm Hg.
"How do I apply a format to a vector of values in IML? In the DATA step, I can just call the PUTN function.” This question came from a SAS customer that I met recently at a conference. My reply? Use the PUTN function, but send it a vector of
The R You Ready blog posed an interesting problem. Essentially, you have a vector that contains n(n+1)/2 elements, and you want to pack those elements into the upper left triangular portion of a matrix. For example, if your data are proc iml; /** vector v is given: ncol(v) = n(n+1)/2 for
When programmers begin learning a new computer language, the first program they write is often one that prints the text “Hello, World!” Successfully writing a Hello World program assures the programmer that the software is successfully installed and that all necessary features are working: parsers, compilers, linkers, and so on.
I just returned home from Vancouver, British Columbia, where I attended the 2010 Joint Statistical Meetings (JSM). I heard that more than 5,300 statisticians attended this year, including about 40 or so from SAS. I stayed busy. I gave a presentation on techniques for visualizing time series, gave a two-hour