Publications by Nick Horton
Example 8.10: Combination dotplot/boxplot (teaching graphic in honor of World Statistics Day)
In honor of World Statistics Day and the read paper that my co-authors Chris Wild, Maxine Pfannkuch, Matt Regan, and I are presenting at the Royal Statistical Society today, we present the R code to generate a combination dotplot/boxplot that is useful for students first learning statistics. One of the over-riding themes of the paper...
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Reader suggestions on alternative ways to create combination dotplot/boxplot
Kudos to several of our readers, who suggested simpler ways to craft the graphical display (combination dotplot/boxplot) from our most recent example.Yihui Xie combines a boxplot with a coarsened version of the PCS scores (using the round() function) used in the stripchart() function.ds = read.csv("http://www.math.smith.edu/r/data/help.csv") smal...
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Example 8.11: violin plots
We’ve continued to get useful feedback and ideas from our posts on the combination dotplot/boxplot and other ways to craft similar displays. Another notion is the violin plot, which combines a boxplot and a (doubled) kernel density plot. While the basic notion of the violin plot does not include the individual points, such a display has virtu...
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Example 8.14: generating standardized regression coefficients
Standardized (or beta) coefficients from a linear regression model are the parameter estimates obtained when the predictors and outcomes have been standardized to have variance = 1. Alternatively, the regression model can be fit and then standardized post-hoc based on the appropriate standard deviations. The parameters are thus interpreted as c...
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A plea for consistent style!
As we get close to the end of the year, it’s time to look back over the past year and think of resolutions for 2011 and beyond. One that’s often on my mind relates to ways to structure my code to make it clearer to others (as well as to myself when I look back upon it months later).Style guides are common in many programming langu...
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Tools to tidy up R code
Last week we made an impassioned plea for attention to style in formatting R and SAS code.While it’s always better to adopt a consistent style and use it whenever you write code, the reality is that sometimes formatting slips (or you end up repurposing code that others wrote. In those situations, the formatR package (due to Yihui X...
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Example 8.19: Referencing lists of variables
In section 1.11.4 (p. 50), we discuss referring to lists of variables in a data set. In SAS, this can be done for variable stored in adjacent columns with the “var_x — var_y” syntax and for variables with sequentially enumerated suffixes with the “var_n1 – var_n2” syntax. We state in the above referenced section that R h...
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Example 8.21: latent class analysis
Latent class analysis is a technique used to classify observations based on patterns of categorical responses. Collins and Lanza’s book,”Latent Class and Latent Transition Analysis,” provides a readable introduction, while the UCLA ATS center has an online statistical computing seminar on the topic.We consider an example analysis from the ...
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Example 8.22: latent class modeling using randomLCA
In Example 8.21 we described how to fit a latent class model to data from the HELP dataset using SAS and R. Subjects were classified based on their observed (manifest) status on the following variables (on street or in shelter in past 180 days [homeless], CESD scores above 20, received substance abuse treatment [satreat], or linked t...
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Example 8.23: Expanding latent class model results
In Example 8.21 we described how to fit a latent class model to data from the HELP dataset using SAS and R (using poLCA(), and then followed up in example 8.22 using randomLCA(). In both entries, we classified subjects based on their observed (manifest) status on the following variables (on street or in shelter in past 180 days [home...
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