sca {multiblock} | R Documentation |
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 |
|
ncomp |
|
scale |
|
samplelinked |
|
... |
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