mcoa: Multiple Co-Inertia Analysis - MCOA

In multiblock: Multiblock Data Fusion in Statistics and Machine Learning

Multiple Co-Inertia Analysis - MCOA

Description

This is a wrapper for the RGCCA::rgcca function for computing MCOA.

Usage

mcoa(X, ncomp = 2, scale = FALSE, verbose = FALSE, ...)

Arguments

X	list of input blocks.
ncomp	integer number of components to extract.
scale	logical indicating if variables should be scaled.
verbose	logical indicating if diagnostic information should be printed.
...	additional arguments for RGCCA.

Details

MCOA resembles GCA and MFA in that it creates a set of reference scores, for which each block's individual scores should correlate maximally too, but also the variance within each block should be taken into account. A single component solution is equivalent to a PCA on concatenated blocks scaled by the so called inverse inertia.

Value

multiblock object including relevant scores and loadings. Relevant plotting functions: multiblock_plots and result functions: multiblock_results.

References

• Le Roux; B. and H. Rouanet (2004). Geometric Data Analysis, From Correspondence Analysis to Structured Data Analysis. Dordrecht. Kluwer: p.180.

• Greenacre, Michael and Blasius, Jörg (editors) (2006). Multiple Correspondence Analysis and Related Methods. London: Chapman & Hall/CRC.

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.mcoa   <- mcoa(potList)
plot(scores(pot.mcoa), labels="names")

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