The DO Loop
Statistical programming in SAS with an emphasis on SAS/IML programs![Critical values of the Kolmogorov-Smirnov test](https://blogs.sas.com/content/iml/files/2019/05/KolmogorovD5-640x336.png)
Recently I wrote about how to compute the Kolmogorov D statistic, which is used to determine whether a sample has a particular distribution. One of the beautiful facts about modern computational statistics is that if you can compute a statistic, you can use simulation to estimate the sampling distribution of
![What is Kolmogorov's D statistic?](https://blogs.sas.com/content/iml/files/2019/05/KolmogorovD1-640x336.png)
Have you ever run a statistical test to determine whether data are normally distributed? If so, you have probably used Kolmogorov's D statistic. Kolmogorov's D statistic (also called the Kolmogorov-Smirnov statistic) enables you to test whether the empirical distribution of data is different than a reference distribution. The reference distribution
![Write to a SAS data set from inside a SAS/IML loop](https://blogs.sas.com/content/iml/files/2017/01/ProgrammingTips-2.png)
In SAS/IML programs, a common task is to write values in a matrix to a SAS data set. For some programs, the values you want to write are in a matrix and you use the CREATE FROM/APPEND FROM syntax to create the data set, as follows: proc iml; X =
![Discrimination, accuracy, and stability in binary classifiers](https://blogs.sas.com/content/iml/files/2019/05/LogiStability3-640x336.png)
At SAS Global Forum 2019, Daymond Ling presented an interesting discussion of binary classifiers in the financial industry. The discussion is motivated by a practical question: If you deploy a predictive model, how can you assess whether the model is no longer working well and needs to be replaced? Daymond
![How to simulate data from a generalized linear model](https://blogs.sas.com/content/iml/files/2019/05/LogiSimExtraVar-640x336.png)
Here's a simulation tip: When you simulate a fixed-effect generalized linear regression model, don't add a random normal error to the linear predictor. Only the response variable should be random. This tip applies to models that apply a link function to a linear predictor, including logistic regression, Poisson regression, and
![Encodings of CLASS variables in SAS regression procedures: A cheat sheet](https://blogs.sas.com/content/iml/files/2017/01/ProgrammingTips-2.png)
SAS regression procedures support several parameterizations of classification variables. When a categorical variable is used as an explanatory variable in a regression model, the procedure generates dummy variables that are used to construct a design matrix for the model. The process of forming columns in a design matrix is called