Hierarchical Principal component analysis - HPCA

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

Usage

hpca(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.

Value

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

Details

HPCA is a hierarchical PCA analysis which combines two or more blocks into a two-level decomposition with block-wise loadings and scores and superlevel common loadings and scores. The method is closely related to the supervised method MB-PLS in structure.

References

Westerhuis, J.A., Kourti, T., and MacGregor,J.F. (1998). Analysis of multiblock and hierarchical PCA and PLS models. Journal of Chemometrics, 12, 301–321.

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