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bootsPLS (version 1.1.2)

Bootstrap Subsamplings of Sparse Partial Least Squares - Discriminant Analysis for Classification and Signature Identification

Description

Applicable to any classification problem with more than 2 classes. It relies on bootstrap subsamplings of sPLS-DA and provides tools to select the most stable variables (defined as the ones consistently selected over the bootstrap subsamplings) and to predict the class of test samples.

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Version

Install

install.packages('bootsPLS')

Monthly Downloads

48

Version

1.1.2

License

GPL-3

Maintainer

F.Rohart

Last Published

May 17th, 2018

Functions in bootsPLS (1.1.2)

spls.hybrid

spls.hybrid, midway between PLS and sPLS
variable.selection

Tune the number of variables on each component
plot.component.selection

Plot the results of the testing procedure to determine the number of component to select
bootsPLS-package

bootsPLS
bootsPLS

Performs replications of sPLSDA on random subsamplings of the data
CI.prediction

Compute Confidence Intervals (CI) for test samples
MSC

Mesenchymal Stem Cells data
plot.variable.selection

Plot the results of the testing procedure to determine the number of variables to select
compile.bootsPLS.object

Combine several bootsPLS objects into one
prediction

prediction
component.selection

Tune the number of components
plot.predictCI

Plot confidence Intervals
fit.model

Create a spls.constraint object by fitting a constraint spls on a bootsPLS object
plot.bootsPLS

Plot the frequency of selection of all variables for all the PLS-component.