The DO Loop
Statistical programming in SAS with an emphasis on SAS/IML programs![Create a deviation plot to visualize values relative to a baseline](https://blogs.sas.com/content/iml/files/2020/03/DeviationPlot1-600x336.png)
A colleague recently posted an article about how to use SAS Visual Analytics to create a circular graph that displays a year's worth of temperature data. Specifically, the graph shows the air temperature for each day in a year relative to some baseline temperature, such as 65F (18C). Days warmer
![The binormal model for ROC curves](https://blogs.sas.com/content/iml/files/2020/02/ROCBinormal3-480x336.png)
The ROC curve is a graphical method that summarizes how well a binary classifier can discriminate between two populations, often called the "negative" population (individuals who do not have a disease or characteristic) and the "positive" population (individuals who do have it). As shown in a previous article, there is
![Visualization of a binary classification analysis](https://blogs.sas.com/content/iml/files/2020/02/classifyTNFP2-640x336.png)
The purpose of this article is to show how to use SAS to create a graph that illustrates a basic idea in a binary classification analysis, such as discriminant analysis and logistic regression. The graph, shown at right, shows two populations. Subjects in the "negative" population do not have some
![A list of SAS DATA step functions that do not run in CAS](https://blogs.sas.com/content/iml/files/2020/02/functionNotCAS3-702x336.png)
Are you a statistical programmer whose company has adopted SAS Viya? If so, you probably know that the DATA step can run in parallel in SAS Cloud Analytic Services (CAS). As Sekosky (2017) says, "running in a single thread in SAS is different from running in many threads in CAS."
![Visualize collinearity diagnostics](https://blogs.sas.com/content/iml/files/2020/02/CollinPlot1-640x336.png)
A previous article shows how to interpret the collinearity diagnostics that are produced by PROC REG in SAS. The process involves scanning down numbers in a table in order to find extreme values. This can be a tedious and error-prone process. Friendly and Kwan (2009) compare this task to a
![The Johnson system: Which distribution should you choose to model data?](https://blogs.sas.com/content/iml/files/2020/02/johnsonratio1-640x336.png)
The Johnson system (Johnson, 1949) contains a family of four distributions: the normal distribution, the lognormal distribution, the SB distribution, and the SU distribution. Previous articles explain why the Johnson system is useful and show how to use PROC UNIVARIATE in SAS to estimate parameters for the Johnson SB distribution