% Generated by roxygen2: do not edit by hand

% Please edit documentation in R/tuneCluster.block.spls.R

\name{tuneCluster.block.spls}

\alias{tuneCluster.block.spls}

\title{Feature Selection Optimization for block (s)PLS method}

\usage{

tuneCluster.block.spls(

  X,

  Y = NULL,

  indY = NULL,

  ncomp = 2,

  test.list.keepX = NULL,

  test.keepY = NULL,

  ...

)

}

\arguments{

\item{X}{list of numeric matrix (or data.frame) with features in columns and samples in rows (with samples order matching in all data sets).}

\item{Y}{(optional) numeric matrix (or data.frame) with features in columns and samples in rows (same rows as \code{X}).}

\item{indY}{integer, to supply if Y is missing, indicates the position of the matrix response in the list \code{X}.}

\item{ncomp}{integer, number of component to include in the model}

\item{test.list.keepX}{list of integers with the same size as X. Each entry corresponds to the different keepX value to test for each block of \code{X}.}

\item{test.keepY}{only if Y is provideid. Vector of integer containing the different value of keepY to test for block \code{Y}.}

\item{...}{other parameters to be included in the spls model (see \code{mixOmics::block.spls})}

}

\value{

\item{silhouette}{silhouette coef. computed for every combinasion of keepX/keepY}

\item{ncomp}{number of component included in the model}

\item{test.keepX}{list of tested keepX}

\item{test.keepY}{list of tested keepY}

\item{block}{names of blocks}

\item{slopes}{"slopes" computed from the silhouette coef. for each keepX and keepY, used to determine the best keepX and keepY}

\item{choice.keepX}{best \code{keepX} for each component}

\item{choice.keepY}{best \code{keepY} for each component}

}

\description{

This function identify the number of feautures to keep per component and thus by cluster in \code{mixOmics::block.spls} by optimizing the silhouette coefficient, which assesses the quality of clustering.

}

\details{

For each component and for each keepX/keepY value, a spls is done from these parameters.

Then the clustering is performed and the silhouette coefficient is calculated for this clustering.

We then calculate "slopes" where keepX/keepY are the coordinates and the silhouette is the intensity.

A z-score is assigned to each slope.

We then identify the most significant slope which indicates a drop in the silhouette coefficient and thus a deterioration of the clustering.

}

\examples{

demo <- suppressWarnings(get_demo_cluster())

X <- list(X = demo$X, Z = demo$Z)

Y <- demo$Y

test.list.keepX <- list("X" = c(5,10,15,20), "Z" = c(2,4,6,8))

test.keepY <- c(2:5)

# tuning

tune.block.spls <- tuneCluster.block.spls(X= X, Y= Y, 

                                          test.list.keepX= test.list.keepX, 

                                          test.keepY= test.keepY, 

                                          mode= "canonical")

keepX <- tune.block.spls$choice.keepX

keepY <- tune.block.spls$choice.keepY

# final model

block.spls.res <- mixOmics::block.spls(X= X, Y= Y, keepX = keepX, 

                             keepY = keepY, ncomp = 2, mode = "canonical")

# get clusters and plot longitudinal profile by cluster

block.spls.cluster <- getCluster(block.spls.res)

}

\seealso{

\code{\link[mixOmics]{block.spls}}, \code{\link[timeOmics]{getCluster}}, \code{\link[timeOmics]{plotLong}}

}