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

## Tag: **Matrix Computations**

The SAS/IML language enables you to perform matrix-vector computations. However, it also provides a convenient "shorthand notation" that enables you to perform elementwise operation on rows or columns in a natural way. You might know that the SAS/IML language supports subscript reduction operators to compute basic rowwise or columnwise quantities.

In a previous post, I discussed computing regression coefficients in different polynomial bases and showed how the coefficients change when you change the basis functions. In particular, I showed how to convert the coefficients computed in one basis to coefficients computed with respect to a different basis. It turns out

I am pleased to announce that the fine folks at SAS Press have made Chapter 2 of my book, Statistical Programming with SAS/IML Software available as a free PDF document. The chapter is titled "Getting Started with the SAS/IML Matrix Programming Language," and it features More than 60 fully functional

Suppose that you compute the coefficients of a polynomial regression by using a certain set of polynomial effects and that I compute coefficients for a different set of polynomial effects. Can I use my coefficients to find your coefficients? The answer is yes, and this article explains how. Standard Polynomial

I was recently asked how to create a tridiagonal matrix in SAS/IML software. For example, how can you easily specify the following symmetric tridiagonal matrix without typing all of the zeros? proc iml; m = {1 6 0 0 0, 6 2 7 0 0, 0 7 3 8 0,

A previous post described a simple algorithm for generating Fibonacci numbers. It was noted that the ratio between adjacent terms in the Fibonacci sequence approaches the "Golden Ratio," 1.61803399.... This post explains why. In a discussion with my fellow blogger, David Smith, I made the comment "any two numbers (at