Publications by Ken Kleinman
Proc report for simple statistics
Ken Beath, of Macquarie University, commented on an earlier entry that the best way to generate summary statistics is using proc report. While the best tools might differ, depending on the purpose, we wanted to share Ken’s code demonstrating how to replicate the R mosaic package tables using proc report.SASKen’s fully annotated c...
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Example 9.13: Negative binomial regression with proc mcmc
In practice, data that derive from counts rarely seem to be fit well by a Poisson model; one more flexible alternative is a negative binomial model. In this SAS-only entry, we discuss how proc mcmc can be used for estimation. An overview of support for Bayesian methods in R can be found in the Bayesian Task View. SASAs noted in example 8.30, th...
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Example 9.15: Bar chart with error bars ("Dynamite plot")
The “dynamite plot”, a bar chart plotting the a mean with a error bar, is one of the most reviled types of image among statisticians. Reasons to dislike them are numerous, and are nicely summarized here. (Edward Tufte also suggests they be avoided.) Nonetheless, as consulting statisticians, we’re often required to meet the needs of our co...
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Example 9.16: Small multiples
Small multiples are one of the great ideas of graphics visionary Edward Tufte (e.g., in Envisioning Information). Briefly, the idea is that if many variations on a theme are presented, differences quickly become apparent. Today we offer general guidance on creating figures with small multiples. As an example, we’ll show graphics for the popu...
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Example 9.18: Constructing the fastest relay team via enumeration
In competitive swimming, the medley relay is a team event in which four different swimmers each swim one of the four strokes: freestyle, breaststroke, backstroke, and butterfly. In general, every swimmer might be able swim any given stroke. How can we choose the fastest relay team? Here we solve this by enumerating all possible tea...
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Example 9.19: Demonstrating the central limit theorem
A colleague recently asked “why should the average get closer to the mean when we increase the sample size?” We should interpret this question as asking why the standard error of the mean gets smaller as n increases. The central limit theorem shows that (under certain conditions, of course) the standard error must do this, and that the mean...
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SAS Macro Simplifies SAS and R integration
Many of us feel very enthusiastic about R. It’s free, it features cutting edge applications, it has a large community of users contributing for mutual benefit, and on and on. There are also many things to like about SAS, including stability, backwards compatibility, and professional support among them. The way to be the best analy...
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RStudio in the cloud, for dummies
You can have your own cloud computing version of R, complete with RStudio. Why should you? It’s cool! Plus, there’s a lot more power out there than you can easily get on your own hardware. And, it’s R in a web page. Run it from your tablet. Run it from work, even if you’re not supposed to install software. Run it from y...
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Example 9.21: The birthday "problem" re-examined
The so-called birthday paradox or birthday problem is simply the counter-intutitive discovery that the probability of (at least) two people in a group sharing a birthday goes up surprisingly fast as the group size increases. If the group is only 23 people, there is a 50% chance that two of them share a birthday, and with 40 people it’s about 90...
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Example 9.23: Demonstrating proportional hazards
A colleague recently asked after a slide suitable for explaining proportional hazards. In particular, she was concerned that her audience not focus on the time to event or probability of the event. An initial thought was to display the cumulative hazards, which have a constant proportion if the model is true. But the colleague’s audience mig...
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