https://github.com/friendly/candisc
Visualizing Generalized Canonical Discriminant and Canonical Correlation Analysis
https://github.com/friendly/candisc
dimension-reduction multivariate-linear-models visualization
Last synced: about 1 year ago
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Visualizing Generalized Canonical Discriminant and Canonical Correlation Analysis
- Host: GitHub
- URL: https://github.com/friendly/candisc
- Owner: friendly
- Created: 2017-09-15T16:10:12.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2024-05-07T02:59:52.000Z (almost 2 years ago)
- Last Synced: 2024-09-21T13:20:15.924Z (over 1 year ago)
- Topics: dimension-reduction, multivariate-linear-models, visualization
- Language: R
- Homepage: https://friendly.github.io/candisc/
- Size: 30.1 MB
- Stars: 15
- Watchers: 6
- Forks: 4
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
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README
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# candisc 
**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")`.