nplstoolbox

Creation and selection of N-way Partial Least Squares (NPLS) models

https://github.com/grvanderploeg/nplstoolbox

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Creation and selection of N-way Partial Least Squares (NPLS) models

README.Rmd

---
output: github_document
---



```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

# NPLStoolbox 



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## Overview

The `NPLStoolbox` allows researchers to use the N-way Partial Least Squares method for their multi-way data.

-   `ncrossreg()` allows the user to identify the appropriate number of NPLS components for their data.
-   `triPLS1()` allows the user to create an NPLS model.
-   `npred()` allows the user to predict y for new data.

This package also comes with two example datasets:

-   `Cornejo2025`: a clinical observational cohort study of 39 transgender persons starting gender-affirming hormone therapy, containing longitudinally measured tongue microbiome, salivary microbiome, salivary cytokine, salivary biochemistry, and circulatory hormone levels (doi TBD).
-   `Jakobsen2025`: an observational cohort of 169 mother-infant dyads investigating the effect of maternal obesity on human milk and the infant gut microbiome .

## Documentation

A basic introduction to the package using the example dataset is given in `vignette("Introduction")`.

This vignette and all function documentation can be found [here](https://grvanderploeg.com/NPLStoolbox/).

## Installation

The `NPLStoolbox` package can be installed from CRAN using:

``` r
install.packages("NPLStoolbox")
```

## Development version

You can install the development version of NPLStoolbox from [GitHub](https://github.com/) with:

``` r
# install.packages("pak")
pak::pak("GRvanderPloeg/NPLStoolbox")
```

## Usage

```{r usage}
library(parafac4microbiome)
library(NPLStoolbox)
set.seed(123)

# Process one of the data cubes from Cornejo2025
processedTongue = processDataCube(Cornejo2025$Tongue_microbiome, sparsityThreshold=0.5, considerGroups=TRUE, groupVariable="GenderID", centerMode=1, scaleMode=2)

# Prepare Y: binarized gender identity
Y = as.numeric(as.factor(Cornejo2025$Tongue_microbiome$mode1$GenderID))
Ycnt = Y - mean(Y)

# Make a one-component NPLS model
model = triPLS1(processedTongue$data, Ycnt, 1)
```

## Getting help

If you encounter an unexpected error or a clear bug, please file an issue with a minimal reproducible example here on [Github](https://github.com/GRvanderPloeg/NPLStoolbox/issues). For questions or other types of feedback, feel free to send an email.

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