cca: Canonical Correlation Analysis - CCA

In multiblock: Multiblock Data Fusion in Statistics and Machine Learning

Canonical Correlation Analysis - CCA

Description

This is a wrapper for the stats::cancor function for computing CCA.

Usage

cca(X)

Arguments

X	list of input data blocks.

Details

CCA is a method which maximises correlation between linear combinations of the columns of two blocks, i.e. max(cor(X1 x a, X2 x b)). This is done sequentially with deflation in between, such that a sequence of correlations and weight vectors a and b are associated with a pair of matrices.

Value

multiblock object with associated with printing, scores, loadings. Relevant plotting functions: multiblock_plots and result functions: multiblock_results.

References

Hotelling, H. (1936) Relations between two sets of variates. Biometrika, 28, 321–377.

See Also

Overviews of available methods, multiblock, and methods organised by main structure: basic, unsupervised, asca, supervised and complex. Common functions for computation and extraction of results and plotting are found in multiblock_results and multiblock_plots, respectively.

Examples

data(potato)
X <- potato$Chemical

cca.pot  <- cca(potato[1:2])

multiblock documentation built on Nov. 18, 2023, 5:06 p.m.