candisc-package {candisc} | R Documentation |

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.

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,

`vignette("HE-examples", package="heplots")`

.

Package: | candisc |

Type: | Package |

Version: | 0.8-5 |

Date: | 2021-01-21 |

License: | GPL (>= 2) |

The organization of functions in this package and the heplots package may change in a later version.

Michael Friendly and John Fox

Maintainer: Michael Friendly <friendly@yorku.ca>

Friendly, M. (2007).
HE plots for Multivariate General Linear Models.
*Journal of Computational and Graphical Statistics*, **16**(2) 421–444.
http://datavis.ca/papers/jcgs-heplots.pdf

Friendly, M. & Kwan, E. (2003).
Effect Ordering for Data Displays,
*Computational Statistics and Data Analysis*, **43**, 509-539.
doi: 10.1016/S0167-9473(02)00290-6

Friendly, M. & Sigal, M. (2014). Recent Advances in Visualizing Multivariate Linear Models. *Revista Colombiana de Estadistica* , **37**(2), 261-283. doi: 10.15446/rce.v37n2spe.47934.

Friendly, M. & Sigal, M. (2016). Graphical Methods for Multivariate Linear Models in Psychological Research: An R Tutorial, *The Quantitative Methods for Psychology*, in press.

Gittins, R. (1985). *Canonical Analysis: A Review with Applications in Ecology*,
Berlin: Springer.

`heplot`

for details about HE plots.

`candisc`

, `cancor`

for details about canonical discriminant analysis
and canonical correlation analysis.

[Package *candisc* version 0.8-5 Index]