vcdExtra-package: Extensions and additions to vcd: Visualizing Categorical Data
In friendly/vcdExtra: 'vcd' Extensions and Additions

vcdExtra-package

R Documentation

Extensions and additions to vcd: Visualizing Categorical Data

Description

This package provides additional data sets, documentation, and a few functions designed to extend the vcd package for Visualizing Categorical Data and the gnm package for Generalized Nonlinear Models. In particular, vcdExtra extends mosaic, assoc and sieve plots from vcd to handle glm() and gnm() models and adds a 3D version in mosaic3d.

This package is also a support package for the book, Discrete Data Analysis with R by Michael Friendly and David Meyer, Chapman & Hall/CRC, 2016, https://www.routledge.com/Discrete-Data-Analysis-with-R-Visualization-and-Modeling-Techniques-for/Friendly-Meyer/9781498725835 with a number of additional data sets, and functions. The web site for the book is http://ddar.datavis.ca.

In addition, I teach a course, Psy 6136: Categorical Data Analysis, https://friendly.github.io/psy6136/ using this package.

Details

The main purpose of this package is to serve as a sandbox for introducing extensions of mosaic plots and related graphical methods that apply to loglinear models fitted using glm() and related, generalized nonlinear models fitted with gnm() in the gnm-package package. A related purpose is to fill in some holes in the analysis of categorical data in R, not provided in base R, the vcd, or other commonly used packages.

The method mosaic.glm extends the mosaic.loglm method in the vcd package to this wider class of models. This method also works for the generalized nonlinear models fit with the gnm-package package, including models for square tables and models with multiplicative associations.

mosaic3d introduces a 3D generalization of mosaic displays using the rgl package.

In addition, there are several new data sets, a tutorial vignette,

vcd-tutorial: Working with categorical data with R and the vcd package, vignette("vcd-tutorial", package = "vcdExtra")

and a few functions for manipulating categorical data sets and working with models for categorical data.

A new class, glmlist, is introduced for working with collections of glm objects, e.g., Kway for fitting all K-way models from a basic marginal model, and LRstats for brief statistical summaries of goodness-of-fit for a collection of models.

For square tables with ordered factors, Crossings supplements the specification of terms in model formulas using Symm, Diag, Topo, etc. in the gnm-package.

Some of these extensions may be migrated into vcd or gnm.

A collection of demos is included to illustrate fitting and visualizing a wide variety of models:

mental-glm: Mental health data: mosaics for glm() and gnm() models
occStatus: Occupational status data: Compare mosaic using expected= to mosaic.glm
ucb-glm: UCBAdmissions data: Conditional independence via loglm() and glm()
vision-quasi: VisualAcuity data: Quasi- and Symmetry models
yaish-unidiff: Yaish data: Unidiff model for 3-way table
Wong2-3: Political views and support for women to work (U, R, C, R+C and RC(1) models)
Wong3-1: Political views, support for women to work and national welfare spending (3-way, marginal, and conditional independence models)
housing: Visualize glm(), multinom() and polr() models from example(housing, package="MASS")

Use demo(package="vcdExtra") for a complete current list.

The vcdExtra package now contains a large number of data sets illustrating various forms of categorical data analysis and related visualizations, from simple to advanced. Use data(package="vcdExtra") for a complete list, or datasets(package="vcdExtra") for an annotated one showing the class and dim for each data set.

Author(s)

Michael Friendly

Maintainer: Michael Friendly <friendly AT yorku.ca> || (ORCID)

References

Friendly, M. Visualizing Categorical Data, Cary NC: SAS Institute, 2000. Web materials: http://www.datavis.ca/books/vcd/.

Friendly, M. and Meyer, D. (2016). Discrete Data Analysis with R: Visualization and Modeling Techniques for Categorical and Count Data. Boca Raton, FL: Chapman & Hall/CRC. http://ddar.datavis.ca.

Meyer, D.; Zeileis, A. & Hornik, K. The Strucplot Framework: Visualizing Multi-way Contingency Tables with vcd Journal of Statistical Software, 2006, 17, 1-48. Available in R via vignette("strucplot", package = "vcd")

Turner, H. and Firth, D. Generalized nonlinear models in R: An overview of the gnm package, 2007, http://eprints.ncrm.ac.uk/472/. Available in R via vignette("gnmOverview", package = "gnm").