unsupervised: Unsupervised Multiblock Methods

In multiblock: Multiblock Data Fusion in Statistics and Machine Learning

Unsupervised Multiblock Methods

Description

Collection of unsupervised multiblock methods:

• SCA - Simultaneous Component Analysis (sca)

• GCA - Generalized Canonical Analysis (gca)

• GPA - Generalized Procrustes Analysis (gpa)

• MFA - Multiple Factor Analysis (mfa)

• PCA-GCA (pcagca)

• DISCO - Distinctive and Common Components with SCA (disco)

• HPCA - Hierarchical Principal component analysis (hpca)

• MCOA - Multiple Co-Inertia Analysis (mcoa)

• JIVE - Joint and Individual Variation Explained (jive)

• STATIS - Structuration des Tableaux à Trois Indices de la Statistique (statis)

• HOGSVD - Higher Order Generalized SVD (hogsvd)

Details

Original documentation of STATIS: statis. JIVE, STATIS and HOGSVD assume variable linked matrices/data.frames, while SCA handles both links.

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)

# Variable linked data
data(candies)
candyList <- lapply(1:nlevels(candies$candy),function(x)candies$assessment[candies$candy==x,])
can.statis <- statis(candyList)
plot(can.statis$statis)

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