<!-- badges: start --> [![Lifecycle: stable](https://img.shields.io/badge/lifecycle-stable-brightgreen.svg)](https://lifecycle.r-lib.org/articles/stages.html#stable) [![CRAN_Status_Badge](http://www.r-pkg.org/badges/version/candisc)](https://cran.r-project.org/package=candisc) [![](https://cranlogs.r-pkg.org/badges/grand-total/candisc)](https://cran.r-project.org/package=candisc) [![](https://img.shields.io/badge/documentation-blue)](https://friendly.github.io/candisc) [![](https://friendly.r-universe.dev/badges/candisc)](https://friendly.r-universe.dev) <!-- badges: end --> # candisc <img src="man/figures/logo.png" align="right" height="160px" /> **Visualizing Generalized Canonical Discriminant and Canonical Correlation Analysis** Version 0.7.0 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 `mlm`s 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")`.