Programming Tips

Strengthen your programming skills with tips and techniques from the experts

Programming Tips
Ron Cody 0
Two macros for detecting data errors

Last year, I wrote a blog demonstrating how to use the %Auto_Outliers macro to automatically identify possible data errors. This blog demonstrates a different approach—one that is useful for variables for which you can identify reasonable ranges of values for each variable. For example, you would not expect resting heart

Programming Tips
Ron Cody 0
Summarizing data

Because it is near the end of the year, I thought a blog about "Summarizing" data might be in order. For these examples, I am going to use a simulated data set called Drug_Study, containing some categorical and numerical variables. For those interested readers, the SAS code that I used

Programming Tips
Ron Cody 0
Creating Simulated Data Sets

There are times when it is useful to simulate data. One of the reasons I use simulated data sets is to demonstrate statistical techniques such as multiple or logistic regression. By using SAS random functions and some DATA step logic, you can create variables that follow certain distributions or are

Programming Tips
Kim Wilson 0
Debugging a stored-process problem

Have you ever submitted a stored process, and instead of the expected output, you saw errors or no output at all? Depending on how you submit the stored process, various logs are available to assist you with debugging. This article provides guidance for understanding which situations call for which logs, where to find each log, and what you should look for in each log.

Analytics | Programming Tips
Rick Wicklin 0
Crossover and mutation: An introduction to two operations in genetic algorithms

This article uses an example to introduce to genetic algorithms (GAs) for optimization. It discusses two operators (mutation and crossover) that are important in implementing a genetic algorithm. It discusses choices that you must make when you implement these operations. Some programmers love using genetic algorithms. Genetic algorithms are heuristic

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