The DO Loop

Programming Tips

Strengthen your programming skills with tips and techniques from the experts

Rick WicklinJanuary 29, 2018 16

How to use the new random-number generators in SAS

What is a random number generator? What are the random-number generators in SAS, and how can you use them to generate random numbers from probability distributions? In SAS 9.4M5, you can use the STREAMINIT function to select from eight random-number generators (RNGs), including five new RNGs. After choosing an RNG,

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Programming Tips

Rick WicklinJanuary 24, 2018 0

Use lists to pass parameters to SAS/IML functions

A popular way to use lists in the SAS/IML language is to pack together several related matrices into a single data structure that can be passed to a function. Imagine that you have written an algorithm that requires a dozen different parameters. Historically, you would have to pass those parameters

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Programming Tips

Rick WicklinJanuary 22, 2018 1

Create lists by using a natural syntax in SAS/IML

SAS/IML 14.3 (SAS 9.4M5) introduced a new syntax for creating lists and for assigning and extracting item in a list. Lists (introduced in SAS/IML 14.2) are data structures that are convenient for holding heterogeneous data. A single list can hold character matrices, numeric matrices, scalar values, and other lists, as

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Analytics | Programming Tips

Rick WicklinJanuary 15, 2018 4

Data unavailable? Use the "eyeball distribution" to simulate

Last week I got the following message: Dear Rick: How can I create a normal distribution within a specified range (min and max)? I need to simulate a normal distribution that fits within a specified range. I realize that a normal distribution is by definition infinite... Are there any alternatives,

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Learn SAS | Programming Tips

Rick WicklinJanuary 10, 2018 4

10 posts from 2017 that deserve a second look

Last week I wrote about the 10 most popular articles from The DO Loop in 2017. My most popular articles tend to be about elementary statistics or SAS programming tips. Less popular are the articles about advanced statistical and programming techniques. However, these technical articles fill an important niche. Not

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Data Visualization | Learn SAS | Programming Tips

Rick WicklinJanuary 8, 2018 6

Label multiple regression lines in SAS

A SAS programmer asked how to label multiple regression lines that are overlaid on a single scatter plot. Specifically, he asked to label the curves that are produced by using the REG statement with the GROUP= option in PROC SGPLOT. He wanted the labels to be the slope and intercept

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Learn SAS | Programming Tips

Rick WicklinJanuary 3, 2018 3

The top 10 posts from The DO Loop in 2017

I wrote more than 100 posts for The DO Loop blog in 2017. The most popular articles were about SAS programming tips, statistical data analysis, and simulation and bootstrap methods. Here are the most popular articles from 2017 in each category. General SAS programming techniques INTCK and INTNX: Do you

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Learn SAS | Programming Tips

Rick WicklinDecember 13, 2017 4

How to get the current TITLE in SAS

The SAS language is large. Even after 20+ years of using SAS, there are many features that I have never used. Recently it became necessary for me to learn about DICTIONARY tables in PROC SQL (and the associated SASHELP views) because I needed to programmatically obtain the text for the

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Programming Tips

Rick WicklinDecember 4, 2017 9

Mean imputation in SAS

Imputing missing data is the act of replacing missing data by nonmissing values. Mean imputation replaces missing data in a numerical variable by the mean value of the nonmissing values. This article shows how to perform mean imputation in SAS. It also presents three statistical drawbacks of mean imputation. How

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Data Visualization | Programming Tips

Rick WicklinNovember 29, 2017 1

Visualize patterns of missing values

Missing values present challenges for the statistical analyst and data scientist. Many modeling techniques (such as regression) exclude observations that contain missing values, which can reduce the sample size and reduce the power of a statistical analysis. Before you try to deal with missing values in an analysis (for example,

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