Function reference • multiblock

Multiblock object A common object type for many multiblock methods.
`multiblock`	multiblock
`scores(<multiblock>)` `loadings(<multiblock>)` `print(<multiblock>)` `summary(<multiblock>)`	Result Functions for Multiblock Objects
`scoreplot(<multiblock>)` `loadingplot(<multiblock>)` `loadingweightplot()` `biplot(<multiblock>)` `corrplot()`	Plot Functions for Multiblock Objects
Basic methods Single- and two-block methods
`basic`	Single- and Two-Block Methods
`pca()`	Principal Component Analysis - PCA
`reexports` `pcr` `plsr` `MSEP` `R2` `RMSEP` `coefplot` `cvsegments` `loading.weights` `loadingplot` `loadings` `mvrValstats` `predplot` `scoreplot` `scores` `validationplot`	Objects exported from other packages
`cca()`	Canonical Correlation Analysis - CCA
`ifa()`	Inter-battery Factor Analysis - IFA
`gsvd()`	Generalised Singular Value Decomposition - GSVD
Unsupervised methods
`unsupervised`	Unsupervised Multiblock Methods
`sca()`	Simultaneous Component Analysis - SCA
`gca()`	Generalized Canonical Analysis - GCA
`gpa()`	Generalized Procrustes Analysis - GPA
`mfa()`	Multiple Factor Analysis - MFA
`pcagca()`	PCA-GCA
`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
Supervised methods
`supervised`	Supervised Multiblock Methods
`mbpls()`	Multiblock Partial Least Squares - MB-PLS
`smbpls()`	Sparse Multiblock Partial Least Squares - sMB-PLS
`sopls()`	Sequential and Orthogonalized PLS (SO-PLS)
`predict(<sopls>)` `coef(<sopls>)` `print(<sopls>)` `summary(<sopls>)` `classify()` `R2(<sopls>)` `RMSEP(<sopls>)` `pcp()` `cvanova()` `print(<cvanova>)` `summary(<cvanova>)` `plot(<cvanova>)`	Result functions for SO-PLS models
`loadings(<sopls>)` `scores(<sopls>)` `scoreplot(<sopls>)` `loadingplot(<sopls>)` `corrplot(<sopls>)` `biplot(<sopls>)`	Scores, loadings and plots for sopls objects
`maage()` `maageSeq()`	Måge plot
`popls()`	Parallel and Orthogonalised Partial Least Squares - PO-PLS
`rosa()`	Response Oriented Sequential Alternation - ROSA
`predict(<rosa>)` `coef(<rosa>)` `print(<rosa>)` `summary(<rosa>)` `blockexpl()` `print(<rosaexpl>)` `rosa.classify()` `scores(<rosa>)` `loadings(<rosa>)`	Result functions for ROSA models
`image(<rosa>)` `barplot(<rosa>)`	Plotting functions for ROSA models
`mbrda()`	Multiblock Redundancy Analysis - mbRDA
Complex methods
`complex`	Methods With Complex Linkage
`lpls()`	L-PLS regression
`plot(<lpls>)` `predict(<lpls>)` `lplsCV()`	Result functions for L-PLS objects (`lpls`)
`lplsData()`	L-PLS data simulation for exo-type analysis
`sopls_pm()` `print(<SO_TDI>)` `sopls_pm_multiple()` `print(<SO_TDI_multiple>)`	Total, direct, indirect and additional effects in SO-PLS-PM.
ASCA
`asca()`	Analysis of Variance Simultaneous Component Analysis - ASCA
`print(<asca>)` `summary(<asca>)` `print(<summary.asca>)` `loadings(<asca>)` `scores(<asca>)` `projections()`	ASCA Result Methods
`loadingplot(<asca>)` `scoreplot(<asca>)`	ASCA Result Methods
Utility functions
`block.data.frame()`	Block-wise indexable data.frame
`compnames()`	Vector of component names
`dummycode()`	Dummy-coding of a single vector
`explvar()`	Explained predictor variance
`mcolors()`	Colour palette generation from matrix of RGB values
`reexports` `pcr` `plsr` `MSEP` `R2` `RMSEP` `coefplot` `cvsegments` `loading.weights` `loadingplot` `loadings` `mvrValstats` `predplot` `scoreplot` `scores` `validationplot`	Objects exported from other packages
`unique_combos()`	Unique combinations of blocks
Datasets
`candies`	Sensory assessment of candies.
`potato`	Sensory, rheological, chemical and spectroscopic analysis of potatoes.
`simulated`	Data simulated to have certain characteristics.
`wine`	Wines of Val de Loire

Reference

Multiblock object

Basic methods

Unsupervised methods

Supervised methods

Complex methods

ASCA

Utility functions

Datasets