Rick Wicklin, PhD, is a distinguished researcher in computational statistics at SAS and is a principal developer of SAS/IML software. His areas of expertise include computational statistics, simulation, statistical graphics, and modern methods in statistical data analysis. Rick is author of the books Statistical Programming with SAS/IML Software and Simulating Data with SAS.
A SAS programmer wanted to display a table in which the rows have different formats. An example is shown below. The programmer wanted columns that represent statistics and rows that represent variables. She wanted to display formats (such as DOLLAR) for some variables—but only for certain statistics. For example, the
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
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
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
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
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
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
Last week Warren Kuhfeld wrote about a graph called the "lines plot" that is produced by SAS/STAT procedures in SAS 9.4M5. (Notice that the "lines plot" has an 's'; it is not a line plot!) The lines plot is produced as part of an analysis that performs multiple comparisons of
The article "Fisher's transformation of the correlation coefficient" featured a Monte Carlo simulation that generated sample correlations from bivariate normal data. The simulation used three steps: Simulate B samples of size N from a bivariate normal distribution with correlation ρ. Use PROC CORR to compute the sample correlation matrix for
Correlations between variables are typically displayed in a matrix. Because the correlation matrix is determined by the order of the variables, it is difficult to find the largest and smallest correlations, which is why analysts sometimes use colors to visualize the correlation matrix. Another visualization option is the pairwise correlation
