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
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
One of the highly visible changes in SAS 9.3 is the fact that the old LISTING destination is no longer the default destination for ODS output. Instead, the HTML destination is the default. One positive consequence of this is that ODS graphics and tables are interlaced in the output. Another
Exploring correlation between variables is an important part of exploratory data analysis. Before you start to model data, it is a good idea to visualize how variables related to one another. Zach Mayer, on his Modern Toolmaking blog, posted code that shows how to display and visualize correlations in R.
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
As I was reviewing notes for my course "Data Simulation for Evaluating Statistical Methods in SAS," I realized that I haven't blogged about simulating categorical data in SAS. This article corrects that oversight. An Easy Way and a Harder Way SAS software makes it easy to sample from discrete "named"
Jittering. To a statistician, it is more than what happens when you drink too much coffee. Jittering is the act of adding random noise to data in order to prevent overplotting in statistical graphs. Overplotting can occur when a continuous measurement is rounded to some convenient unit. This has the
In my statistical analysis of coupons article, I presented a scatter plot that includes the identity line, y=x. This post describes how to write a general program that uses the SGPLOT procedure in SAS 9.2. By a "general program," I mean that the program produces the result based on the
This is Part 4 of my response to Charlie Huang's interesting article titled Top 10 most powerful functions for PROC SQL. As I did for eaerlier topics, I will examine one of the "powerful" SQL functions that Charlie mentions and show how to do the same computation in SAS/IML software.
