In example 7.20, we showed how to simulate categorical data. But we might anticipate needing to do that frequently. If a SAS function weren't built in and an equivalent R function not available in a package, we could build them from scratch.SASThe SAS code is particularly tortured, since we must ...

Both SAS and R provide means of simulating categorical data (see section 1.10.4). Alternatively, it is trivial to write code to do this directly. In this entry, we show how to do it once. In a future entry, we'll demonstrate writing a SAS Macro (section A.8.1) and a function in R (...

Suppose we need to find the closest pair of observations on some variable x. For example, we might be concerned that some data had been accidentally duplicated. We return the ID's of the two closest observations, and their distance from each other. In both languages, we'll first create the data, ...

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When displaying contingency tables (section 2.3.1), there are times when it is useful to either show or hide the missing data category. Both SAS and the typical R command default to displaying the table only for observations where both factors are observed.In this example, we generate some multinomial data (section 1.10.4) ...

The plot of Amazon sales rank over time generated in example 7.14 leaves questions. From a software perspective, we'd like to make the plot prettier, while we can embellish the plot to inform our interpretation about how the rank is calculated.For the latter purpose, we'll create an indicator of whether ...

In this example, we show a simple plot of the sales rank data read in as shown in example 7.13.SASIn SAS, we use the symbol statement (section 5.3) to request small (with the h option) dots (with the v option, and that the dots not be connected (with the i option. (...

In example 7.8, we used built-in functions to produce an empirical CDF plot. But the empirical cumulative distribution function (CDF) is simple to calculate directly, and it might be useful to have more control over its appearance than is afforded by...

In our previous entry, we described how to generate a dataset from SAS that could be used for analyses in R. Alternatively, someone primarily using R might want to test the new ”statistical graphics” procedures available starting with SAS 9.2. Her...

Some people use both SAS and R in their daily work. They might be more familiar with SAS as a tool for manipulating data and R preferable for plotting purposes. While our goal in the book is to enable people to avoid having to switch back and forth, ...

The empirical cumulative density function (CDF) (section 5.1.16) is a useful way to compare distributions between populations. The Kolmogorov-Smirnov (section 2.4.2) statistic D is the value of x with the maximum distance between the two curves. As an...

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Suppose we wanted to assess the probability P(X=x) for a binomial random variate with n = 10 and with p = .81, .84, ..., .99. This could be helpful, for example, in various game settings. In SAS, we find the probability that X=x using differences in t...

In honor of Amazon's official release date for the book, we offer this blog entry.Both SAS and R can be used to find the Amazon Sales Rank for a book by downloading the desired web page and ferreting out the appropriate line. This code is likely to br...

The scatterplot in section 7.4 is a plot we could use repeatedly. We demonstrate how to create a macro (SAS, section A.8) and a function (R, section B.5) to do it more easily.SAS%macro logiplot(x=x, y=y, data=, jitterwidth=.05, smooth=50);data lp1;set...

The plot in section 7.3 has some problems. At the very least, the jittered values ought to be between 0 and 1, so the smoothed lines fit better with them. Once again we use the data generated in section 7.2 as an example. For both SAS and R, we use conditioning (section 1.11.2) to make ...

It's useful to look at scatterplots even when the "y" variable is dichotomous. For example, this can help determine whether categorization or linear assumptions would be more plausible. However, an unmodified scatterplot is less than helpful, since all of the "y" values are either 0 or 1, and are hard to separate ...

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It might be useful to be able to simulate data from a logistic regression (section 4.1.1). Our process is to generate the linear predictor, then apply the inverse link, and finally draw from a distribution with this parameter. This approach is useful in that it can easily be applied to other ...