basic: Single- and Two-Block Methods

In multiblock: Multiblock Data Fusion in Statistics and Machine Learning

Single- and Two-Block Methods

Description

This documentation covers a range of single- and two-block methods. In particular:

• PCA - Principal Component Analysis (pca)

• PCR - Principal Component Regression (pcr)

• PLSR - Partial Least Squares Regression (plsr)

• CCA - Canonical Correlation Analysis (cca)

• IFA - Interbattery Factor Analysis (ifa)

• GSVD - Generalized SVD (gsvd)

See Also

Overviews of available methods, multiblock, and methods organised by main structure: basic, unsupervised, asca, supervised and complex.

Examples

data(potato)
X <- potato$Chemical
y <- potato$Sensory[,1,drop=FALSE]

pca.pot  <- pca(X, ncomp = 2)
pcr.pot  <- pcr(y ~ X, ncomp = 2)
pls.pot  <- plsr(y ~ X, ncomp = 2)
cca.pot  <- cca(potato[1:2])
ifa.pot  <- ifa(potato[1:2])
gsvd.pot <- gsvd(lapply(potato[3:4], t))

multiblock documentation built on April 4, 2025, 2:20 a.m.