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Tip revision: 31ee86eb84ac4ecddba48ac1cdc645c8399e9e8f authored by LingsongMeng on 03 May 2020, 04:04:37 UTC
Tip revision: 31ee86e
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/GuidedSparseKmeans.KLam.R2out.R
GuidedSparseKmeans.KLam.R2out(x, R2.per, pre.K = NULL,,
  nstart = 20, maxiter = 15, silence = F)
\item{x}{Gene expression matrix, n*p (rows for subjects and columns for genes).}

\item{R2.per}{R-squared or pseudo R-squared between phenotypic variable and expression value of each gene, a vector.}

\item{pre.K}{Pre-knowledge of the number of clusters.}

\item{}{A proper value of the boundary of l1n weights.}

\item{nstart}{Specify the number of starting point for K-means.}

\item{maxiter}{Maximum number of iteration.}

\item{silence}{Output progress or not.}
A list consisting of
\item{}{value of selected K.}
\item{}{value of selected lam.}
\item{ARI.Cs}{Adjusted ARI values for cluster results.}
\item{Jaccard.gene}{Jaccard index values for gene selection results.}
Selection of Tuning Parameter K and lam in Guided Sparse K-means
(R-square or pseudo R-square is from the outside)
Select tuning parameter K using gap statistics and tuning parameter lam using sensitivity analysis in Guided Sparse K-means integrating clinical dataset with gene expression dataset,
R-square or pseudo R-square is from the outside, not calculated in the function.
Lingsong Meng
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