About this blog
Rick Wicklin, PhD, is a senior 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, and modern methods in statistical data analysis. Rick is author of the book Statistical Programming with SAS/IML Software.
Follow @RickWicklin on Twitter.
For each observation, find the variable that contains the minumum value
The other day I encountered an article in the SAS Knowledge Base that shows how to write a macro that “returns the variable name that contains the maximum or minimum value across an observation.” Some people might say that the macro is “clever.” I say it is complicated. This is [...]
Post a Comment Expand data by using frequencies
A reader asked: I want to create a vector as follows. Suppose there are two given vectors x=[A B C] and f=[1 2 3]. Here f indicates the frequency vector. I hope to generate a vector c=[A B B C C C]. I am trying to use the REPEAT function [...]
Post a Comment Extending SAS: How to define new functions in PROC FCMP and SAS/IML software
SAS software provides many run-time functions that you can call from your SAS/IML or DATA step programs. The SAS/IML language has several hundred built-in statistical functions, and Base SAS software contains hundreds more. However, it is common for statistical programmers to extend the run-time library to include special user-defined functions. [...]
Post a Comment BY-group processing in SAS/IML
Because the SAS/IML language is a general purpose programming language, it doesn’t have a BY statement like most other SAS procedures (such as PROC REG). However, there are several ways to loop over categorical variables and perform an analysis on the observations in each category. One way is to use [...]
Post a Comment The Poissonness plot: A goodness-of-fit diagnostic
Last week I discussed how to fit a Poisson distribution to data. The technique, which involves using the GENMOD procedure, produces a table of some goodness-of-fit statistics, but I find it useful to also produce a graph that indicates the goodness of fit. For continuous distributions, the quantile-quantile (Q-Q) plot [...]
Post a Comment A singular spectrum analysis of a temperature time series
Last week I blogged about how to construct a smoother for a time series for the temperature in Albany, NY from 1995 to March, 2012. I smoothed the data by “folding” the time series into a single “year” that contains repeated measurements for each day of the year. Experts in [...]
Post a Comment Creating a periodic smoother
In yesterday’s post, I discussed a “quick and dirty” method to smooth periodic data. However, after I smoothed the data I remarked that the smoother itself was not exactly periodic. At the end points of the periodic interval, the smoother did not have equal slopes and the method does not [...]
Post a Comment Smoothers for periodic data
Over at the SAS and R blog, Ken Kleinman discussed using polar coordinates to plot time series data for multiple years. The time series plot was reproduced in SAS by my colleague Robert Allison. The idea of plotting periodic data on a circle is not new. In fact it goes [...]
Post a Comment Fitting a Poisson distribution to data in SAS
Over at the SAS Discussion Forums, someone asked how to use SAS to fit a Poisson distribution to data. The questioner asked how to fit the distribution but also how to overlay the fitted density on the data and to create a quantile-quantile (Q-Q) plot. The questioner mentioned that the [...]
Post a Comment Count missing values in observations
Locating missing values is important in statistical data analysis. I’ve previously written about how to count the number of missing values for each variable in a data set. In Base SAS, I showed how to use the MEANS or FREQ procedures to count missing values. In the SAS/IML language, I [...]
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