The DO Loop
Statistical programming in SAS with an emphasis on SAS/IML programs
A statistical programmer asked how to simulate event-trials data for groups. The subjects in each group have a different probability of experiencing the event. This article describes one way to simulate this scenario. The simulation is similar to simulating from a mixture distribution. This article also shows three different ways
A colleague spent a lot of time creating a panel of graphs to summarize some data. She did not use SAS software to create the graph, but I used SAS to create a simplified version of her graph, which is shown to the right. (The colors are from her graph.)
The number of possible bootstrap samples for a sample of size N is big. Really big. Recall that the bootstrap method is a powerful way to analyze the variation in a statistic. To implement the standard bootstrap method, you generate B random bootstrap samples. A bootstrap sample is a sample
You can use the bootstrap method to estimate confidence intervals. Unlike formulas, which assume that the data are drawn from a specified distribution (usually the normal distribution), the bootstrap method does not assume a distribution for the data. There are many articles about how to use SAS to bootstrap statistics
For graphing multivariate data, it is important to be able to convert the data between "wide form" (a separate column for each variable) and "long form" (which contains an indicator variable that assigns a group to each observation). If the data are numeric, the wide data can be represented as
This article shows how to create a "sliced survival plot" for proportional-hazards models that are created by using PROC PHREG in SAS. Graphing the result of a statistical regression model is a valuable way to communicate the predictions of the model. Many SAS procedures use ODS graphics to produce graphs
