I'm pleased to announce the release of the vcdExtra package, v. 0.5-0
from R-Forge to CRAN, on its way to a CRAN server near you.
vcdExtra was originally designed to serve as a sandbox for introducing
extensions of mosaic plots and other visualizations for categorical
data, particularly those that apply to (poisson surrogate)
loglinear models fitted using glm() and
related, generalized nonlinear models fitted with gnm() in the gnm
package. See package?vcdExtra for information and an overview.
Heather Turner, Achim Zeileis and Duncan Murdoch contributed substantially
to the latest release.
In addition, it contains a tutorial vignette,
"Working with categorical data with R and the vcd package",
vignette("vcd-tutorial", package = "vcdExtra")
many new data sets (with worked examples), and a collection of demos
illustrating things that didn't fit into the standard .Rd format.
New in recent releases are:
o An initial implementation of 3D mosaic plots, using the rgl package. See
?mosaic3d, example(mosaic3d) and demo("mosaic3d-hec").
o The method mosaic.glm extends the mosaic.loglm method in the vcd
package to wider class of models fit using glm() and gnm() for
non-negative
responses. There are also extensions of association plots (assoc) and
sieve
diagrams (sieve) for these models. See demo("mental-glm"),
demo("vision-quasi")
and others.
o Some initial steps in collecting, handling and summarizing sets of
related
glm() and loglm() models provided by the functions glmlist(), loglmlist()
summarize() methods, Kway() for fitting all k-way glm() models, etc.
--
Michael Friendly Email: friendly AT yorku DOT ca
Professor, Psychology Dept.
York University Voice: 416 736-5115 x66249 Fax: 416 736-5814
4700 Keele Street http://www.math.yorku.ca/SCS/friendly.html
Toronto, ONT M3J 1P3 CANADA
______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
