% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/M_ABCpp.R
\name{f.clustABC.MultiSource}
\alias{f.clustABC.MultiSource}
\title{Consensus clustering from stacked ABC label matrices}
\usage{
f.clustABC.MultiSource(
  res,
  numclust = NULL,
  dissimilarity = "mabc",
  linkage = "ward",
  alpha = 0.625,
  mds = FALSE
)
}
\arguments{
\item{res}{Integer label matrix (\code{0} = object not selected).}

\item{numclust}{Optional number of clusters to cut the tree into.}

\item{dissimilarity}{\code{"mabc"} (arc-cosine, default) or \code{"abc"} (linear).}

\item{linkage}{AGNES agglomeration method.}

\item{alpha}{Flexible-linkage parameter.}

\item{mds}{Logical; draw an MDS of the dissimilarities.}
}
\value{
A list with \code{DistM}, \code{Clust} and (if \code{numclust} given)
\code{cut}.
}
\description{
Turns a row-stacked matrix of per-iteration cluster labels
(\eqn{K \cdot R} rows by \eqn{N} columns) into a co-clustering dissimilarity
and applies \code{cluster::agnes}. \eqn{S_{ij}} is the co-clustering
probability over iterations selecting both objects (B6). The co-clustering
counts use the compiled C++ kernel \code{count_coclustering_cpp}
(from \code{src/mcpp_sampling.cpp}) when available, otherwise a pure-R loop.
}
\references{
\insertCite{Amaratunga2008}{MosaiClusteR}
}
\seealso{
\code{\link{M_ABCpp}}
}
