% 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}}
}