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+++ b/man/tuneCluster.spca.Rd
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+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/tuneCluster.spca.R
+\name{tuneCluster.spca}
+\alias{tuneCluster.spca}
+\title{Feature Selection Optimization for sPCA method}
+\usage{
+tuneCluster.spca(X, ncomp = 2, test.keepX = rep(ncol(X), ncomp), ...)
+}
+\arguments{
+\item{X}{numeric matrix (or data.frame) with features in columns and samples in rows}
+
+\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{...}{other parameters to be included in the spls model (see \code{mixOmics::spca})}
+}
+\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{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}
+}
+\description{
+This function identify the number of feautures to keep per component and thus by cluster in \code{mixOmics::spca} 
+by optimizing the silhouette coefficient, which assesses the quality of clustering.
+}
+\details{
+For each component and for each keepX 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 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$X
+
+# tuning
+tune.spca.res <- tuneCluster.spca(X = X, ncomp = 2, test.keepX = c(2:10))
+keepX <- tune.spca.res$choice.keepX
+plot(tune.spca.res)
+
+# final model
+spca.res <- mixOmics::spca(X=X, ncomp = 2, keepX = keepX)
+plotLong(spca.res)
+}