R: Extensions and additions to vcd: Visualizing Categorical Data

vcdExtra-package {vcdExtra} | 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 now a support package for the book, Discrete Data Analysis with R by Michael Friendly and David Meyer, Chapman & Hall/CRC, 2016, https://www.crcpress.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.

### Details

Package: | vcdExtra
Type: | Package
Version: | 0.7-1
Date: | 2017-09-28
License: | GPL version 2 or newer
LazyLoad: | yes

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 goodnes-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>

### References

Friendly, M. Visualizing Categorical Data, Cary NC: SAS Insitute, 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")`.

### See Also

`gnm-package`, for an extended range of models for contingency tables

`mosaic` for details on mosaic displays within the strucplot framework.

### Examples

    example(mosaic.glm)

    demo("mental-glm")

[Package vcdExtra version 0.7-1 Index]