The most common way to read observations from a SAS data set into SAS/IML matrices is to read all of the data at once by using the ALL clause in the READ statement. However, the READ statement also has options that do not require holding all of the observations in
In a previous post, I showed how to read data from a SAS data set into SAS/IML matrices or vectors. This article shows the converse: how to use the CREATE, APPEND, and CLOSE statements to create a SAS data set from data stored in a matrix or in vectors. Creating
Statistical programmers can be creative and innovative. But when it comes to choosing names of variables, often x1, x2, x3,... works as well as any other choice. In this blog post, I have two tips that are related to constructing variable names of the form x1, x2,..., xn. Both tips
As Cat Truxillo points out in her recent blog post, some SAS procedures require data to be in a "long" (as opposed to "wide") format. Cat uses a DATA step to convert the data from wide to long format. Although there is nothing wrong with this approach, I prefer to
Sample covariance matrices and correlation matrices are used frequently in multivariate statistics. This post shows how to compute these matrices in SAS and use them in a SAS/IML program. There are two ways to compute these matrices: Compute the covariance and correlation with PROC CORR and read the results into
Often, the first step of a SAS/IML program is to use the USE, READ, and CLOSE statements to read data from a SAS data set into a vector or matrix. There are several ways to read data: Read variables into vectors of the same name. Read one or more variables
My mother taught me to put things away when I'm finished using them. She doesn't use a computer, but if she did, I know that she'd approve of this tip from my book: Tip: Always close your files and data sets when you are finished reading or writing them. In
