Functions for computing and visualizing generalized canonical discriminant analyses and canonical correlation analysis for a multivariate linear model. 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 higher-way 'MANOVA' designs for all factors in a multivariate linear model, computing canonical scores and vectors for each term. The graphic functions provide low-rank (1D, 2D, 3D) visualizations of terms in an 'mlm' via the 'plot.candisc' and 'heplot.candisc' methods. Related plots are now provided for canonical correlation analysis when all predictors are quantitative.
|Author||Michael Friendly [aut, cre], John Fox [aut]|
|Date of publication||2016-11-11 00:07:23|
|Maintainer||Michael Friendly <email@example.com>|
|License||GPL (>= 2)|
cancor: Canonical Correlation Analysis
candisc: Canonical discriminant analysis
candiscList: Canonical discriminant analyses
candisc-package: Visualizing Generalized Canonical Discriminant and Canonical...
can_lm: Transform a Multivariate Linear model mlm to a Canonical...
dataIndex: Indices of observations in a model data frame
Grass: Yields from Nitrogen nutrition of grass species
heplot.cancor: Canonical Correlation HE plots
heplot.candisc: Canonical Discriminant HE plots
heplot.candiscList: Canonical Discriminant HE plots
HSB: High School and Beyond Data
plot.cancor: Canonical Correlation Plots
redundancy: Canonical Redundancy Analysis
varOrder: Order variables according to canonical structure or other...
vecscale: Scale vectors to fill the current plot
vectors: Draw Labeled Vectors in 2D or 3D
Wilks: Wilks Lambda Tests for Canonical Correlations
Wine: Chemical composition of three cultivars of wine
Wolves: Wolf skulls