Abstract
In the first seven chapters of this book, we have treated R like a traditional statistical software program and reviewed how it can perform data management, report simple statistics, and estimate a variety of regression models. In the remainder of this book, though, we turn to the added flexibility that R offers—both in terms of programming capacity that is available for the user as well as providing additional applied tools through packages. In this chapter, we focus on how loading additional batches of code from user-written packages can add functionality that many software programs will not allow. Although we have used packages for a variety of purposes in the previous seven chapters (including car, gmodels, and lattice, to name a few), here we will highlight packages that enable unique methods. While the CRAN website lists numerous packages that users may install at any given time, we will focus on four particular packages to illustrate the kinds of functionality that can be added.
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Notes
- 1.
Note that this multilevel approach to panel data is most sensible for short panels such as these where there are many individuals relative to the number of time points. For long panels in which there are many time points relative to the number of individuals, more appropriate models are described as pooled time series cross-section methods. For more on the study of short panels, see Monogan (2011) and Fitzmaurice et al. (2004).
- 2.
If you do not have these data from before, you can download the file BPchap7.dta from the Dataverse on page vii or the chapter content on page 125.
- 3.
See also the nlme library, which was a predecessor to lme4.
- 4.
If you do not have these data from before, you can download the file SinghJTP.dta from the Dataverse on page vii or the chapter content on page 125.
- 5.
For priors on the coefficients, the option b0 sets the vector of means of a multivariate Gaussian prior, and B0 sets the variance-covariance matrix of the multivariate Gaussian prior. The prior distribution of the error variance of regression is inverse Gamma, and this distribution can be manipulated by setting its shape parameter with option c0 and scale parameter with option d0. Alternatively, the inverse Gamma distribution can be manipulated by changing its mean with the option sigma.mu and its variance with the option sigma.var.
- 6.
To write out a similar table to Table 8.2 in LaTeX, load the xtable library in R and type the following into the console:
xtable(cbind(summary(mcmc.hours)$statistics[,1:2], summary(mcmc.hours)$quantiles[,c(1,5)]),digits=4)
- 7.
This frequently occurs when one package depends on code from another.
- 8.
LaLonde’s data is also available in the file LL.csv, available in the Dataverse (see page vii) or the chapter content (see page 125).
- 9.
The UN data is also available in the file UN.csv, available in the Dataverse (see page vii) or the chapter content (see page 125).
- 10.
When choosing how many dimensions to include in a measurement model, many scholars use the “elbow rule,” meaning they do not include any dimensions past a visual elbow in the scree plot. In this case, a scholar certainly would not include more than three dimensions, and may be content with two. Another common cutoff is to include any dimension for which the eigenvalue exceeds 1, which would have us stop at two dimensions.
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Monogan, J.E. (2015). Using Packages to Apply Advanced Models. In: Political Analysis Using R. Use R!. Springer, Cham. https://doi.org/10.1007/978-3-319-23446-5_8
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