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Histogram of data overlaid with a beta density curve, fitted by maximum likelihood estimation

Rick WicklinNovember 27, 2017 2

The method of moments: A smart way to choose initial parameters for MLE

When you run an optimization, it is often not clear how to provide the optimization algorithm with an initial guess for the parameters. A good guess converges quickly to the optimal solution whereas a bad guess might diverge or require many iterations to converge. Many people use a default value

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Programming Tips

Rick WicklinNovember 22, 2017 2

Compute the CDF and quantiles of discrete distributions

A statistical programmer read my article about the beta-binomial distribution and wanted to know how to compute the cumulative distribution (CDF) and the quantile function for this distribution. In general, if you know the PDF for a discrete distribution, you can also compute the CDF and quantile functions. This article

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Analytics | Programming Tips

Beta-binomial distribution and expected values in SAS

Rick WicklinNovember 20, 2017 9

Simulate data from the beta-binomial distribution in SAS

This article shows how to simulate beta-binomial data in SAS and how to compute the density function (PDF). The beta-binomial distribution is a discrete compound distribution. The "binomial" part of the name means that the discrete random variable X follows a binomial distribution with parameters N (number of trials) and

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Learn SAS | Programming Tips

Rick WicklinNovember 15, 2017 5

Catch run-time errors in SAS/IML programs

Did you know that a SAS/IML function can recover from a run-time error? You can specify how to handle run-time errors by using a programming technique that is similar to the modern "try-catch" technique, although the SAS/IML technique is an older implementation. Preventing errors versus handling errors In general, SAS/IML

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Programming Tips

The PAUSE statement as a debugging tool in SAS/IML Studio

Rick WicklinNovember 13, 2017 2

A tip for debugging SAS/IML modules: The PAUSE statement

Debugging is the bane of every programmer. SAS supports a DATA step debugger, but that debugger can't be used for debugging SAS/IML programs. In lieu of a formal debugger, many SAS/IML programmers resort to inserting multiple PRINT statements into a function definition. However, there is an easier way to query

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Learn SAS | Programming Tips

Rick WicklinNovember 8, 2017 0

How to format rows of a table in SAS

A SAS programmer wanted to display a table in which the rows have different formats. An example is shown below. The programmer wanted columns that represent statistics and rows that represent variables. She wanted to display formats (such as DOLLAR) for some variables—but only for certain statistics. For example, the

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Programming Tips

Rick WicklinNovember 6, 2017 0

What is a factoid in SAS?

Have you ever seen the "Fit Summary" table from PROC LOESS, as shown to the right? Or maybe you've seen the "Model Information" table that is displayed by some SAS analytical procedures? These tables provide brief interesting facts about a statistical procedure, hence they are called factoids. In SAS, a

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Programming Tips

Rick WicklinNovember 1, 2017 0

Evaluate a function by using the function name in SAS/IML

A SAS/IML programmer asked whether you can pass the name of a function as an argument to a SAS/IML module and have the module call the function that is passed in. The answer is "yes." The basic idea is to create a string that represents the function call and then

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Data Visualization | Programming Tips

Rick WicklinOctober 30, 2017 1

A SAS programming technique to modify ODS templates

This article demonstrates a SAS programming technique that I call Kuhfeld's template modification technique. The technique enables you to dynamically modify an ODS template and immediately call the modified template to produce a new graph or table. By following the five steps in this article, you can implement the technique

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Programming Tips

Rick WicklinOctober 11, 2017 0

Simulate correlations by using the Wishart distribution

The article "Fisher's transformation of the correlation coefficient" featured a Monte Carlo simulation that generated sample correlations from bivariate normal data. The simulation used three steps: Simulate B samples of size N from a bivariate normal distribution with correlation ρ. Use PROC CORR to compute the sample correlation matrix for

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