Simultaneous Component Analysis - SCA

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

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).

Value

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

and result functions: multiblock_results.

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.

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
#> Simultaneous Component Analysis 
#> 
#> Call:
#> sca(X = candyList, samplelinked = FALSE)