| 883f7f27 |
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/PLSDA_class.R
|
| 8db793ae |
\name{PLSDA}
|
| 883f7f27 |
\alias{PLSDA}
|
| d0f81a51 |
\title{Partial least squares discriminant analysis}
|
| 8db793ae |
\usage{
|
| 46c79734 |
PLSDA(number_components = 2, factor_name, pred_method = "max_prob", ...)
|
| 8db793ae |
}
|
| 56a019fe |
\arguments{
|
| 09388373 |
\item{number_components}{(numeric, integer) The number of PLS components. The default is \code{2}.\cr}
|
| a110b7e7 |
|
| d0f81a51 |
\item{factor_name}{(character) The name of a sample-meta column to use.}
|
| a110b7e7 |
|
| 09388373 |
\item{pred_method}{(character) Prediction method. Allowed values are limited to the following: \itemize{ \item{\code{"max_yhat"}: The predicted group is selected based on the largest value of y_hat.}\item{\code{"max_prob"}: The predicted group is selected based on the largest probability of group membership.}} The default is \code{"max_prob"}.}
|
| 46c79734 |
|
| d0f81a51 |
\item{...}{Additional slots and values passed to \code{struct_class}.}
|
| 56a019fe |
}
|
| 7af536f9 |
\value{
|
| 5d51daa4 |
A \code{PLSDA} object with the following \code{output} slots:
\tabular{ll}{
|
| 09b8b680 |
\code{scores} \tab (DatasetExperiment) \cr
|
| 5d51daa4 |
\code{loadings} \tab (data.frame) \cr
\code{yhat} \tab (data.frame) \cr
\code{design_matrix} \tab (data.frame) \cr
\code{y} \tab (data.frame) \cr
\code{reg_coeff} \tab (data.frame) \cr
\code{probability} \tab (data.frame) \cr
\code{vip} \tab (data.frame) \cr
\code{pls_model} \tab (list) \cr
\code{pred} \tab (data.frame) \cr
\code{threshold} \tab (numeric) \cr
|
| 09b8b680 |
\code{sr} \tab (data.frame) Selectivity ratio for a variable represents a measure of a variable's importance in the PLS model. The output data.frame contains a column of selectivity ratios, a column of p-values based on an F-distribution and a column indicating significance at p < 0.05. \cr
\code{sr_pvalue} \tab (data.frame) A p-value computed from the Selectivity Ratio based on an F-distribution. \cr
|
| 5d51daa4 |
}
|
| 7af536f9 |
}
|
| 883f7f27 |
\description{
|
| 46c79734 |
PLS is a multivariate regression technique that extracts latent variables maximising covariance between the input data and the response. The Discriminant Analysis variant uses group labels in the response variable. For >2 groups a 1-vs-all approach is used. Group membership can be predicted for test samples based on a probability estimate of group membership, or the estimated y-value.
|
| d0f81a51 |
}
\details{
|
| 09388373 |
This object makes use of functionality from the following packages:\itemize{ \item{\code{pls}}}
|
| 883f7f27 |
}
|
| 09388373 |
\section{Inheritance}{
A \code{PLSDA} object inherits the following \code{struct} classes: \cr\cr
|
| 55546c0b |
\verb{[PLSDA]} >> \verb{[PLSR]} >> \verb{[model]} >> \verb{[struct_class]}
|
| 09388373 |
}
|
| 5dcc4dc2 |
\examples{
|
| 09388373 |
M = PLSDA(
number_components = 2,
factor_name = "V1",
pred_method = "max_prob")
|
| a110b7e7 |
M = PLSDA('number_components'=2,factor_name='Species')
|
| 5dcc4dc2 |
}
|
| d0f81a51 |
\references{
|
| 32654e96 |
Liland K, Mevik B, Wehrens R (2023). \emph{pls: Partial Least Squares and
|
| 09388373 |
Principal Component Regression}. R package version 2.8-3,
|
| 32654e96 |
\url{https://CRAN.R-project.org/package=pls}.
|
| d0f81a51 |
|
| 32654e96 |
Perez NF, Ferre J, Boque R (2009). "Calculation of the reliability of
classification in discriminant partial least-squares binary
classification." \emph{Chemometrics and Intelligent Laboratory Systems},
\emph{95}(2), 122-128.
|
| d0f81a51 |
|
| 32654e96 |
Barker M, Rayens W (2003). "Partial least squares for discrimination."
\emph{Journal of Chemometrics}, \emph{17}(3), 166-173.
|
| d0f81a51 |
}
|
