In my book Simulating Data with SAS, I show how to use the SAS DATA step to simulate data from a logistic regression model. Recently there have been discussions on the SAS/IML Support Community about simulating logistic data by using the SAS/IML language. This article describes how to efficiently simulate
A colleague asked me an interesting question: I have a journal article that includes sample quantiles for a variable. Given a new data value, I want to approximate its quantile. I also want to simulate data from the distribution of the published data. Is that possible? This situation is common.
I've pointed out in the past that in the SAS/IML language matrices are passed to modules "by reference." This means that large matrices are not copied in and out of modules but are updated "in place." As a result, the SAS/IML language can be very efficient when it computes with
In my book Simulating Data with SAS, I specify how to generate lognormal data with a shape and scale parameter. The method is simple: you use the RAND function to generate X ~ N(μ, σ), then compute Y = exp(X). The random variable Y is lognormally distributed with parameters μ
While at a conference recently, I was asked whether it was possible to use SAS to simulate data from an inverse gamma distribution. The SAS customer had looked at the documentation for the RAND function and did not see "inverse gamma" listed among the possible choices. The answer is "yes."
While at SAS Global Forum 2014 I attended a talk by Jorge G. Morel on the analysis of data with overdispersion. (His slides are available, along with a video of his presentation.) The Wikipedia defines overdispersion as "greater variability than expected from a simple model." For count data, the "simple
I began 2014 by compiling a list of 13 popular articles from my blog in 2013. Although this "People's Choice" list contains many articles that I am proud of, it did not include all of my favorites, so I decided to compile an "Editor's Choice" list. The blog posts on
My previous post described the multinomial distribution and showed how to generate random data from the multinomial distribution in SAS by using the RANDMULTINOMIAL function in SAS/IML software. The RANDMULTINOMIAL function is simple to use and implements an efficient algorithm called the sequential conditional marginal method (see Gentle (2003), p.
This article describes how to generate random samples from the multinomial distribution in SAS. The content is taken from Chapter 8 of my book Simulating Data with SAS. The multinomial distribution is a discrete multivariate distribution. Suppose there are k different types of items in a box, such as a
This article describes how to implement the truncated normal distribution in SAS. Although the implementation in this article uses the SAS/IML language, you can also implement the ideas and formulas by using the DATA step and PROC FCMP. For reference, I recommend the Wikipedia article on the truncated normal distribution.
