The DO Loop
Statistical programming in SAS with an emphasis on SAS/IML programs![What is a factoid in SAS?](https://blogs.sas.com/content/iml/files/2017/01/ProgrammingTips-2.png)
Have you ever seen the "Fit Summary" table from PROC LOESS, as shown to the right? Or maybe you've seen the "Model Information" table that is displayed by some SAS analytical procedures? These tables provide brief interesting facts about a statistical procedure, hence they are called factoids. In SAS, a
![Evaluate a function by using the function name in SAS/IML](https://blogs.sas.com/content/iml/files/2017/11/evalfunc.png)
A SAS/IML programmer asked whether you can pass the name of a function as an argument to a SAS/IML module and have the module call the function that is passed in. The answer is "yes." The basic idea is to create a string that represents the function call and then
![A SAS programming technique to modify ODS templates](https://blogs.sas.com/content/iml/files/2017/10/KuhfeldTMT2-640x336.png)
This article demonstrates a SAS programming technique that I call Kuhfeld's template modification technique. The technique enables you to dynamically modify an ODS template and immediately call the modified template to produce a new graph or table. By following the five steps in this article, you can implement the technique
![Should you use principal component regression? Principal component regression in SAS: Loadings plot](https://blogs.sas.com/content/iml/files/2017/10/pcr3-600x336.png)
This article describes the advantages and disadvantages of principal component regression (PCR). This article also presents alternative techniques to PCR. In a previous article, I showed how to compute a principal component regression in SAS. Recall that principal component regression is a technique for handling near collinearities among the regression
![Principal component regression in SAS Principal component regression in SAS: Loadings plot](https://blogs.sas.com/content/iml/files/2017/10/pcr3-600x336.png)
A common question on discussion forums is how to compute a principal component regression in SAS. One reason people give for wanting to run a principal component regression is that the explanatory variables in the model are highly correlated which each other, a condition known as multicollinearity. Although principal component
![The diffogram and other graphs for multiple comparisons of means Diffogram for multiple comparisons of means in SAS](https://blogs.sas.com/content/iml/files/2017/10/diffogram2-480x336.png)
In a previous article, I discussed the lines plot for multiple comparisons of means. Another graph that is frequently used for multiple comparisons is the diffogram, which indicates whether the pairwise differences between means of groups are statistically significant. This article discusses how to interpret a diffogram. Two related plots