The other day I encountered a SAS Knowledge Base article that shows how to count the number of missing and nonmissing values for each variable in a data set. However, the code is a complicated macro that is difficult for a beginning SAS programmer to understand. (Well, it was hard
Polynomials are used often in data analysis. Low-order polynomials are used in regression to model the relationship between variables. Polynomials are used in numerical analysis for numerical integration and Taylor series approximations. It is therefore important to be able to evaluate polynomials in an efficient manner. My favorite evaluation technique
You can extend the capability of the SAS/IML language by writing modules. A module is a user-defined function. You can define a module by using the START and FINISH statements. Many people, including myself, define modules at the top of the SAS/IML program in which they are used. You can
Do you know someone who has a birthday in mid-September? Odds are that you do: the middle of September is when most US babies are born, according to data obtained from the National Center for Health Statistics (NCHS) Web site (see Table 1-16). There's an easy way to remember this
Looping is essential to statistical programming. Whether you need to iterate over parameters in an algorithm or indices in an array, a loop is often one of the first programming constructs that a beginning programmer learns. Today is the first anniversary of this blog, which is named The DO Loop,
I previously showed how to generate random numbers in SAS by using the RAND function in the DATA step or by using the RANDGEN subroutine in SAS/IML software. These functions generate a stream of random numbers. (In statistics, the random numbers are usually a sample from a distribution such as
You can generate a set of random numbers in SAS that are uniformly distributed by using the RAND function in the DATA step or by using the RANDGEN subroutine in SAS/IML software. (These same functions also generate random numbers from other common distributions such as binomial and normal.) The syntax
NOTE: SAS stopped shipping the SAS/IML Studio interface in 2018. It is no longer supported, so this article is no longer relevant. When I write SAS/IML programs, I usually do my development in the SAS/IML Studio environment. Why? There are many reasons, but the one that I will discuss today
I've previously described ways to solve systems of linear equations, A*b = c. While discussing the relative merits of the solving a system for a particular right hand side versus solving for the inverse matrix, I made the assertion that it is faster to solve a particular system than it
The SAS/IML language provides two functions for solving a nonsingular nxn linear system A*x = c: The INV function numerically computes the inverse matrix, A-1. You can use this to solve for x: Ainv = inv(A); x = Ainv*c;. The SOLVE function numerically computes the particular solution, x, for a
