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Analytics | Learn SAS | Programming Tips

Rick WicklinOctober 3, 2022 1

Compute moments of probability distributions in SAS

A previous article discusses the definitions of three kinds of moments for a continuous probability distribution: raw moments, central moments, and standardized moments. These are defined in terms of integrals over the support of the distribution. Moments are connected to the familiar shape features of a distribution: the mean, variance,

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Learn SAS | Programming Tips

Rick WicklinSeptember 26, 2022 3

Display correlations in a list format

The correlations between p variables are usually displayed by using a symmetric p x p matrix of correlations. However, sometimes you might prefer to see the correlations listed in "long form" as a three-column table, as shown to the right. In this table, each row shows a pair of variables and the

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Learn SAS | Programming Tips

Rick WicklinSeptember 19, 2022 2

Generate random ID values for subjects in SAS

A common question on SAS discussion forums is how to use SAS to generate random ID values. The use case is to generate a set of random strings to assign to patients in a clinical study. If you assign each patient a unique ID and delete the patients' names, you

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Learn SAS | Programming Tips

Rick WicklinSeptember 14, 2022 2

Base 26: A mapping from integers to strings

I recently showed how to represent positive integers in any base and gave examples of base 2 (binary), base 8 (octal), and base 16 (hexadecimal). One fun application is that you can use base 26 to associate a positive integer to every string of English characters. This article shows how

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Learn SAS | Programming Tips

Rick WicklinSeptember 12, 2022 0

Convert integers from base 10 to another base

An integer can be represented in many ways. This article shows how to represent a positive integer in any base b. The most common base is b=10, but other popular bases are b=2 (binary numbers), b=8 (octal), and b=16 (hexadecimal). Each base represents integers in different ways. Think of a

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Analytics | Learn SAS | Programming Tips

Rick WicklinSeptember 7, 2022 0

A test for monotonic sequences and functions

Monotonic transformations occur frequently in math and statistics. Analysts use monotonic transformations to transform variable values, with Tukey's ladder of transformations and the Box-Cox transformations being familiar examples. Monotonic distributions figure prominently in probability theory because the cumulative distribution is a monotonic increasing function. For a continuous distribution that is

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Learn SAS | Programming Tips

Rick WicklinAugust 31, 2022 2

Two types of syntax for the SELECT-WHEN statement in SAS

The SELECT-WHEN statement in the SAS DATA step is an alternative to using a long sequence of IF-THEN/ELSE statements. Although logically equivalent to IF-THEN/ELSE statements, the SELECT-WHEN statement can be easier to read. This article discusses the two distinct ways to specify the SELECT-WHEN statement. You can use the first

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Data Visualization | Programming Tips

Rick WicklinAugust 29, 2022 2

Order the bars in a bar chart with PROC SGPLOT

A SAS programmer was trying to understand how PROC SGPLOT orders categories and segments in a stacked bar chart. As with all problems, it is often useful to start with a simpler version of the problem. After you understand the simpler situation, you can apply that understanding to the more

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Data Visualization | Learn SAS | Programming Tips

Rick WicklinAugust 24, 2022 0

How to stagger labels on an axis in PROC SGPLOT

A SAS programmer asked how to display long labels at irregular locations along the horizontal axis of scatter plot. The labels indicate various phases of a clinical study. This article discusses the problem and shows how to use the FITPOLICY=STAGGER option on the XAXIS or X2AXIS statement to avoid collisions

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Learn SAS | Programming Tips

Rick WicklinJuly 25, 2022 0

Monte Carlo estimates of area

I've previously shown how to use Monte Carlo simulation to estimate probabilities and areas. I illustrated the Monte Carlo method by estimating π ≈ 3.14159... by generating points uniformly at random in a unit square and computing the proportion of those points that were inside the unit circle. The previous

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