--- a
+++ b/man/tuneCluster.spls.Rd
@@ -0,0 +1,71 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/tuneCluster.spls.R
+\name{tuneCluster.spls}
+\alias{tuneCluster.spls}
+\title{Feature Selection Optimization for sPLS method}
+\usage{
+tuneCluster.spls(
+  X,
+  Y,
+  ncomp = 2,
+  test.keepX = rep(ncol(X), ncomp),
+  test.keepY = rep(ncol(Y), ncomp),
+  ...
+)
+}
+\arguments{
+\item{X}{numeric matrix (or data.frame) with features in columns and samples in rows}
+
+\item{Y}{numeric matrix (or data.frame) with features in columns and samples in rows (same rows as \code{X})}
+
+\item{ncomp}{integer, number of component to include in the model}
+
+\item{test.keepX}{vector of integer containing the different value of keepX to test for block \code{X}.}
+
+\item{test.keepY}{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::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::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 <- demo$X
+Y <- demo$Y
+
+# tuning
+tune.spls <- tuneCluster.spls(X, Y, ncomp= 2, test.keepX= c(5,10,15,20), test.keepY= c(2,4,6))
+keepX <- tune.spls$choice.keepX
+keepY <- tune.spls$choice.keepY
+
+# final model
+spls.res <- mixOmics::spls(X, Y, ncomp= 2, keepX= keepX, keepY= keepY)
+
+# get clusters and plot longitudinal profile by cluster
+spls.cluster <- getCluster(spls.res)
+plotLong(spls.res)
+
+
+}
+\seealso{
+\code{\link[mixOmics]{spls}}, \code{\link[timeOmics]{getCluster}}, \code{\link[timeOmics]{plotLong}}
+}