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. This blog focuses on statistical programming. It discusses statistical and computational algorithms, statistical graphics, simulation, efficiency, and data analysis. Rick is author of the books Statistical Programming with SAS/IML Software and Simulating Data with SAS.
Follow @RickWicklin on Twitter.
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Two of my favorite string-manipulation functions in the SAS DATA step are the COUNTW function and the SCAN function. The COUNTW function counts the number of words in a long string of text. Here "word" means a substring that is delimited by special characters, such as a space character, a […]Post a Comment
Every beginning SAS programmer learns the simple IF-THEN/ELSE statement for conditional processing in the SAS DATA step. The basic If-THEN statement handles two cases: if a condition is true, the program does one thing, otherwise the program does something else. Of course, you can handle more cases by using multiple […]Post a Comment
A grid is a set of evenly spaced points. You can use SAS to create a grid of points on an interval, in a rectangular region in the plane, or even in higher-dimensional regions like the parallelepiped shown at the left, which is generated by three vectors. You can use […]Post a Comment
SAS software can fit many different kinds of regression models. In fact a common question on the SAS Support Communities is "how do I fit a <name> regression model in SAS?" And within that category, the most frequent questions involve how to fit various logistic regression models in SAS. There […]Post a Comment
In a previous post I showed how to download, install, and use packages in SAS/IML 14.1. SAS/IML packages incorporate source files, documentation, data sets, and sample programs into a ZIP file. The PACKAGE statement enables you to install, uninstall, and manage packages. You can load functions and data into your […]Post a Comment
Descriptive univariate statistics are the foundation of data analysis. Before you create a statistical model for new data, you should examine descriptive univariate statistics such as the mean, standard deviation, quantiles, and the number of nonmissing observations. In SAS, there is an easy way to create a data set that […]Post a Comment
A dummy variable (also known as indicator variable) is a numeric variable that indicates the presence or absence of some level of a categorical variable. The word "dummy" does not imply that these variables are not smart. Rather, dummy variables serve as a substitute or a proxy for a categorical […]Post a Comment
In the SAS/IML language, you can read data from a SAS data set into a set of vectors (each with their own name) or into a single matrix. Beginning programmers might wonder about the advantages of each approach. When should you read data into vectors? When should you read data […]Post a Comment
Novice SAS programmers quickly learn the advantages of using PROC SORT to sort data, followed by a BY-group analysis of the sorted data. A typical example is to analyze demographic data by state or by ZIP code. A BY statement enables you to produce multiple analyses from a single procedure […]Post a Comment