# The DO Loop

Statistical programming in SAS with an emphasis on SAS/IML programsWhen I wake up early to write my blog, I often wonder, "Is anyone going to read this?" Apparently so. I started writing The DO Loop in September, 2010. Since then, I've posted about 60 entries about statistical programming with SAS/IML software. Since this is a statistical blog, it is

When I finished writing my book, Statistical Programming with SAS/IML Software, I was elated. However, one small task still remained. I had to write the index. How Long Should an Index Be? My editor told me that SAS Press would send the manuscript to a professional editor who would index

Recently, I needed to detect whether a matrix consists entirely of missing values. I wrote the following module: proc iml; /** Module to detect whether all elements of a matrix are missing values. Works for both numeric and character matrices. Version 1 (not optimal) **/ start isMissing(x); if type(x)='C' then

There are three kinds of programming errors: parse-time errors, run-time errors, and logical errors. It doesn't matter what language you are using (SAS/IML, MATLAB, R, C/C++, Java,....), these errors creep up everywhere. Two of these errors cause a program to report an error, whereas the third is more insidious because

Both covariance matrices and correlation matrices are used frequently in multivariate statistics. You can easily compute covariance and correlation matrices from data by using SAS software. However, sometimes you are given a covariance matrix, but your numerical technique requires a correlation matrix. Other times you are given a correlation matrix,

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