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candisc

Visualizing Generalized Canonical Discriminant and Canonical Correlation Analysis

https://github.com/friendly/candisc

Science Score: 26.0%

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Keywords

dimension-reduction multivariate-linear-models visualization

Keywords from Contributors

linear-hypotheses matrices plot repeated-measure-designs visualizing-hypothesis-tests categorical-data-visualization generalized-linear-models mosaic-plots
Last synced: 6 months ago · JSON representation

Repository

Visualizing Generalized Canonical Discriminant and Canonical Correlation Analysis

Basic Info
Statistics
  • Stars: 16
  • Watchers: 5
  • Forks: 4
  • Open Issues: 1
  • Releases: 3
Topics
dimension-reduction multivariate-linear-models visualization
Created over 8 years ago · Last pushed 6 months ago
Metadata Files
Readme Changelog

README.md

Lifecycle: stable CRAN_Status_Badge

candisc

Visualizing Generalized Canonical Discriminant and Canonical Correlation Analysis

Version 0.9.2

This package includes functions for computing and visualizing generalized canonical discriminant analyses and canonical correlation analysis for a multivariate linear model. The goal is to provide ways of visualizing such models in a low-dimensional space corresponding to dimensions (linear combinations of the response variables) of maximal relationship to the predictor variables.

Traditional canonical discriminant analysis is restricted to a one-way MANOVA design and is equivalent to canonical correlation analysis between a set of quantitative response variables and a set of dummy variables coded from the factor variable. The candisc package generalizes this to multi-way MANOVA designs for all terms in a multivariate linear model (i.e., an mlm object), computing canonical scores and vectors for each term (giving a "candiscList" object).

The graphic functions are designed to provide low-rank (1D, 2D, 3D) visualizations of terms in a mlm via the plot.candisc method, and the HE plot heplot.candisc() and heplot3d.candisc() methods. For mlms with more than a few response variables, these methods often provide a much simpler interpretation of the nature of effects in canonical space than heplots for pairs of responses or an HE plot matrix of all responses in variable space.

Analogously, a multivariate linear (regression) model with quantitative predictors can also be represented in a reduced-rank space by means of a canonical correlation transformation of the Y and X variables to uncorrelated canonical variates, Ycan and Xcan. Computation for this analysis is provided by cancor and related methods. Visualization of these results in canonical space are provided by the plot.cancor(), heplot.cancor() and heplot3d.cancor() methods.

These relations among response variables in linear models can also be useful for "effect ordering" (Friendly & Kwan (2003) for variables in other multivariate data displays to make the displayed relationships more coherent. The function varOrder() implements a collection of these methods.

Installation

| | | |---------------------|-----------------------------------------------| | CRAN version | install.packages("candisc") | | Development version | remotes::install_github("friendly/candisc") |

Or, install from r-universe

r install.packages('candisc', repos = c('https://friendly.r-universe.dev')

Vignettes

  • A new vignette, vignette("diabetes", package="candisc"), illustrates some of these methods.

  • A more comprehensive collection of examples is contained in the vignette for the heplots package, browseVignettes(package = "heplots").

Owner

  • Name: Michael Friendly
  • Login: friendly
  • Kind: user
  • Location: Toronto
  • Company: York University

GitHub Events

Total
  • Watch event: 1
  • Push event: 7
Last Year
  • Watch event: 1
  • Push event: 7

Committers

Last synced: over 1 year ago

All Time
  • Total Commits: 231
  • Total Committers: 4
  • Avg Commits per committer: 57.75
  • Development Distribution Score (DDS): 0.476
Past Year
  • Commits: 33
  • Committers: 1
  • Avg Commits per committer: 33.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
friendly f****y@e****b 121
Michael Friendly f****y@y****a 108
jfox j****x@e****b 1
stefan7th s****h@e****b 1
Committer Domains (Top 20 + Academic)
yorku.ca: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 2
  • Total pull requests: 0
  • Average time to close issues: 14 days
  • Average time to close pull requests: N/A
  • Total issue authors: 2
  • Total pull request authors: 0
  • Average comments per issue: 2.5
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • retsej22 (1)
  • d-carlson (1)
Pull Request Authors
  • friendly (1)
Top Labels
Issue Labels
Pull Request Labels

Packages

  • Total packages: 1
  • Total downloads:
    • cran 1,876 last-month
  • Total docker downloads: 43,935
  • Total dependent packages: 6
  • Total dependent repositories: 11
  • Total versions: 21
  • Total maintainers: 1
cran.r-project.org: candisc

Visualizing Generalized Canonical Discriminant and Canonical Correlation Analysis

  • Versions: 21
  • Dependent Packages: 6
  • Dependent Repositories: 11
  • Downloads: 1,876 Last month
  • Docker Downloads: 43,935
Rankings
Docker downloads count: 0.6%
Downloads: 5.9%
Dependent packages count: 7.3%
Average: 8.1%
Dependent repos count: 8.8%
Forks count: 12.2%
Stargazers count: 14.1%
Maintainers (1)
friendly@yorku.ca
Last synced: 6 months ago

Dependencies

DESCRIPTION cran
  • R >= 3.5.0 depends
  • car * depends
  • graphics * depends
  • heplots >= 0.8 depends
  • stats * depends
  • MASS * suggests
  • corrplot * suggests
  • knitr * suggests
  • rgl * suggests
  • rmarkdown * suggests
  • rpart * suggests
  • rpart.plot * suggests
revdep/library/candisc/new/candisc/DESCRIPTION cran
  • R >= 3.5.0 depends
  • heplots >= 0.8 depends
  • car * imports
  • graphics * imports
  • stats * imports
  • MASS * suggests
  • carData * suggests
  • corrplot * suggests
  • knitr * suggests
  • rgl * suggests
  • rmarkdown * suggests
  • rpart * suggests
  • rpart.plot * suggests
revdep/library/candisc/old/candisc/DESCRIPTION cran
  • R >= 3.5.0 depends
  • car * depends
  • graphics * depends
  • heplots >= 0.8 depends
  • stats * depends
  • MASS * suggests
  • corrplot * suggests
  • knitr * suggests
  • rgl * suggests
  • rmarkdown * suggests
  • rpart * suggests
  • rpart.plot * suggests