gca: Generalized Canonical Analysis - GCA

In multiblock: Multiblock Data Fusion in Statistics and Machine Learning

Generalized Canonical Analysis - GCA

Description

This is an interface to both SVD-based (default) and RGCCA-based GCA (wrapping the RGCCA::rgcca function)

Usage

gca(X, ncomp = "max", svd = TRUE, tol = 10^-12, corrs = TRUE, ...)

Arguments

X	list of input blocks.
ncomp	integer number of components to extract, either single integer (equal for all blocks), vector (individual per block) or 'max' for maximum possible number of components.
svd	logical indicating if Singular Value Decomposition approach should be used (default=TRUE).
tol	numeric tolerance for component inclusion (singular values).
corrs	logical indicating if correlations should be calculated for RGCCA based approach.
...	additional arguments for RGCCA approach.

Details

GCA is a generalisation of Canonical Correlation Analysis to handle three or more blocks. There are several ways to generalise, and two of these are available through gca. The default is an SVD based approach estimating a common subspace and measuring mean squared correlation to this. An alternative approach is available through RGCCA. For the SVD based approach, the ncomp parameter controls the block-wise decomposition while the following the consensus decomposition is limited to the minimum number of components from the individual blocks.

Value

multiblock object including relevant scores and loadings. Relevant plotting functions: multiblock_plots and result functions: multiblock_results. blockCoef contains canonical coefficients, while blockDecomp contains decompositions of each block.

References

• Carroll, J. D. (1968). Generalization of canonical correlation analysis to three or more sets of variables. Proceedings of the American Psychological Association, pages 227-22.

• Van der Burg, E. and Dijksterhuis, G. (1996). Generalised canonical analysis of individual sensory profiles and instrument data, Elsevier, pp. 221–258.

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)
potList <- as.list(potato[c(1,2,9)])
pot.gca <- gca(potList)
plot(scores(pot.gca), labels="names")

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