The DO Loop
Statistical programming in SAS with an emphasis on SAS/IML programs
My article about deletion diagnostics investigated how influential an observation is to a least squares regression model. In other words, if you delete the i_th observation and refit the model, what happens to the statistics for the model? SAS regression procedures provide many tables and graphs that enable you to
For linear regression models, there is a class of statistics that I call deletion diagnostics or leave-one-out statistics. These observation-wise statistics address the question, "If I delete the i_th observation and refit the model, what happens to the statistics for the model?" For example: The PRESS statistic is similar to
Recoding variables can be tedious, but it is often a necessary part of data analysis. Almost every SAS programmer has written a DATA step that uses IF-THEN/ELSE logic or the SELECT-WHEN statements to recode variables. Although creating a new variable is effective, it is also inefficient because you have to
A family of curves is generated by an equation that has one or more parameters. To visualize the family, you might want to display a graph that overlays four of five curves that have different parameter values, as shown to the right. The graph shows members of a family of
Statistical programmers and analysts often use two kinds of rectangular data sets, popularly known as wide data and long data. Some analytical procedures require that the data be in wide form; others require long form. (The "long format" is sometimes called "narrow" or "tall" data.) Fortunately, the statistical graphics procedures
Knowing how to visualize a regression model is a valuable skill. A good visualization can help you to interpret a model and understand how its predictions depend on explanatory factors in the model. Visualization is especially important in understanding interactions between factors. Recently I read about work by Jacob A.