The DO Loop
Statistical programming in SAS with an emphasis on SAS/IML programs
The SAS/IML language supports user-defined functions (also called modules). Many SAS/IML programmers know that you can use the RETURN function to return a value from a user-defined function. For example, the following function returns the sum of each column of matrix: proc iml; start ColSum(M); return( M[+, ] ); /*
It is common to want to extract the lower or upper triangular elements of a matrix. For example, if you have a correlation matrix, the lower triangular elements are the nontrivial correlations between variables in your data. As I've written before, you can use the VECH function to extract the
When you are working with probability distributions (normal, Poisson, exponential, and so forth), there are four essential functions that a statistical programmer needs. As I've written before, for common univariate distributions, SAS provides the following functions: the PDF function, which returns the probability density at a given point the CDF
Suppose that you have two data vectors, x and y, with the same number of elements. How can you rearrange the values of y so that they have the same relative order as the values of x? In other words, find a permutation, π, of the elements of y so
I've been working on a new book about Simulating Data with SAS. In researching the chapter on simulation of multivariate data, I've noticed that the probability density function (PDF) of multivariate distributions is often specified in a matrix form. Consequently, the multivariate density can usually be computed by using the
I often use the SAS/IML language for simulating data with certain known properties. In fact, I'm writing a book called Simulating Data with SAS. When I simulate repeated measurements (sometimes called replicated data), I often want to generate an ID variable that identifies which measurement is associated with which subject