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
In the SAS/IML language, the index creation operator (:) is used to construct a sequence of integer values. For example, the expression 1:7 creates a row vector with seven elements: 1, 2, ..., 7. It is important to know the precedence of matrix operators. When I was in grade school,
I've previously discussed how to find the root of a univariate function. This article describes how to find the root (zero) of a function of several variables by using Newton's method. There have been many papers, books, and dissertations written on the topic of root-finding, so why am I blogging
In a previous blog post, I showed how to use the LOGISTIC procedure to construct a receiver operator characteristic (ROC) curve in SAS. That same day, Charlie H. blogged about how to use the DATA step to construct an ROC curve from basic principles. It has been a long time
One of the joys of statistics is that you can often use different methods to estimate the same quantity. Last week I described how to compute a parametric density estimate for univariate data, and use the parameters estimates to compute the area under the probability density function (PDF). This article
The area under a density estimate curve gives information about the probability that an event occurs. The simplest density estimate is a histogram, and last week I described a few ways to compute empirical estimates of probabilities from histograms and from the data themselves, including how to construct the empirical
Readers' comments indicate that my previous blog article about computing the area under an ROC curve was helpful. Great! There is another common application of numerical integration: finding the area under a density estimation curve. This article provides an overview of density estimation and computes an empirical cumulative density function.
