Title:	N-Way Partial Least Squares Modelling of Multi-Way Data
Version:	1.0.0
Description:	Creation and selection of N-way Partial Least Squares (NPLS) models. Selection of the optimal number of components can be done using ncrossreg(). NPLS was originally described by Rasmus Bro, see <doi:10.1002/%28SICI%291099-128X%28199601%2910%3A1%3C47%3A%3AAID-CEM400%3E3.0.CO%3B2-C>.
License:	MIT + file LICENSE
Encoding:	UTF-8
RoxygenNote:	7.3.2
Imports:	dplyr, parafac4microbiome, pracma, rTensor, stats
Depends:	R (≥ 2.10)
LazyData:	true
Suggests:	knitr, rmarkdown, testthat (≥ 3.0.0)
Config/testthat/edition:	3
URL:	https://github.com/GRvanderPloeg/NPLStoolbox, https://grvanderploeg.com/NPLStoolbox/
BugReports:	https://github.com/GRvanderPloeg/NPLStoolbox/issues
VignetteBuilder:	knitr
NeedsCompilation:	no
Packaged:	2025-06-06 11:37:40 UTC; roelv
Author:	Geert Roelof van der Ploeg [aut, cre], Johan Westerhuis [ctb], Anna Heintz-Buschart [ctb], Age Smilde [ctb], University of Amsterdam [cph, fnd]
Maintainer:	Geert Roelof van der Ploeg <g.r.ploeg@uva.nl>
Repository:	CRAN
Date/Publication:	2025-06-10 13:20:05 UTC

Cornejo2025 longitudinal dataset measured in transgender persons

Description

The Cornejo2025 longitudinal dataset as three-dimensional arrays, with subjects in mode 1, features in mode 2 and time in mode 3.

Usage

Cornejo2025

Format

'Cornejo2025'

A list object with three elements:

Tongue_microbiome

List object of the tongue longitudinal microbiota data.

Salivary_microbiome

List object of the saliva longitudinal microbiota data.

Salivary_cytokines

List object of the longitudinal salivary cytokine data.

Salivary_biochemistry

List object of the longitudinal salivary biochemistry data.

Circulatory_hormones

List object of the longitudinal circulatory hormone data.

Clinical_measurements

List object of the longitudinal clinical outcome data.

Subject_metadata

Matrix with subject metadata.

Source

TBD

Cross-validation of NPLS by classical K-fold CV.

Description

This function runs ACMTF-R with cross-validation. A deterministic K–fold partition is used: the subjects are split in order into cvFolds groups. For each fold the training set consists of the other folds and the test set is the current fold.

Usage

ncrossreg(X, y, maxNumComponents = 5, maxIter = 120, cvFolds = dim(X)[1])

Arguments

Value

A list with two elements: - varExp: a tibble with the variance–explained (for X and Y) per number of components. - RMSE: a tibble with the RMSE (computed over the unified CV prediction vector) per number of components.

Examples

set.seed(123)
X <- array(rnorm(25 * 5 * 4), dim = c(25, 5, 4))
y <- rnorm(25)  # Random response variable
result = ncrossreg(X, y, cvFolds=2, maxNumComponents=2)

Predict Y for new data by projecting the data onto the latent space defined by an NPLS model.

Description

Predict Y for new data by projecting the data onto the latent space defined by an NPLS model.

Usage

npred(model, newX)

Arguments

Value

Ypred: vector of the predicted value(s) of Y for the new data

Examples

Y = as.numeric(as.factor(Cornejo2025$Tongue$mode1$GenderID))
Ycnt = Y - mean(Y)
model = triPLS1(Cornejo2025$Tongue$data, Ycnt, numComponents=1)
npred(model, Cornejo2025$Tongue$data[1,,])

Tri-PLS1: three-way PLS regressed onto a y vector

Description

Tri-PLS1: three-way PLS regressed onto a y vector

Usage

triPLS1(X, y, numComponents, tol = 1e-10, maxIter = 100)

Arguments

Value

Model

Examples

set.seed(123)
X <- array(rnorm(100 * 5 * 4), dim = c(100, 5, 4))  # Random tensor (100 samples, 5 vars, 4 vars)
y <- rnorm(100)  # Random response variable
model <- triPLS1(X, y, numComponents = 2)