sca: Simultaneous Component Analysis - SCA

In multiblock: Multiblock Data Fusion in Statistics and Machine Learning

Simultaneous Component Analysis - SCA

Description

This is a basic implementation of the SCA-P algorithm (least restricted SCA) with support for both sample- and variable-linked modes.

Usage

sca(X, ncomp = 2, scale = FALSE, samplelinked = "auto", ...)

Arguments

X	list of input blocks.
ncomp	integer number of components to extract.
scale	logical indicating autoscaling of features (default = FALSE).
samplelinked	character/logical indicating if blocks are linked by samples (TRUE) or variables (FALSE). Using 'auto' (default), this will be determined automatically.
...	additional arguments (not used).

Details

SCA, in its original variable-linked version, calculates common loadings and block-wise scores. There are many possible constraints and specialisations. This implementations uses PCA as the backbone, thus resulting in deterministic, ordered components. A parameter controls the linking mode, but if left untouched an attempt is made at automatically determining variable or sample linking.

Value

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

References

Levin, J. (1966) Simultaneous factor analysis of several gramian matrices. Psychometrika, 31(3), 413–419.

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

# Object linked data
data(potato)
potList <- as.list(potato[c(1,2,9)])
pot.sca    <- sca(potList)
plot(scores(pot.sca), labels="names")

# Variable linked data
data(candies)
candyList <- lapply(1:nlevels(candies$candy),function(x)candies$assessment[candies$candy==x,])
pot.sca    <- sca(candyList, samplelinked = FALSE)
pot.sca

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