% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/M_ABCdist_WC.R
\name{M_ABCdist.WC}
\alias{M_ABCdist.WC}
\title{Multi-source M-ABC fused with WeightedClust}
\usage{
M_ABCdist.WC(
  List,
  distmeasure = rep("euclidean", length(List)),
  weighting = rep("var", length(List)),
  normalize = rep(list(NULL), length(List)),
  gr = c(),
  bag = rep(TRUE, length(List)),
  numsim = 1000L,
  numvar = rep(100L, length(List)),
  linkage = rep("ward.D", length(List)),
  NC = NULL,
  NC2 = NULL,
  StopRange = FALSE,
  wc_weight = seq(1, 0, -0.1),
  wc_weightclust = 0.5,
  wc_linkage = "ward",
  wc_alpha = 0.625,
  mds = FALSE,
  nugget_type = "between",
  nugget_args = list()
)
}
\arguments{
\item{List}{A list of \eqn{K} data matrices over the same objects,
\strong{objects (samples) in rows} and features in columns.}

\item{distmeasure, weighting, normalize, bag, numvar, linkage}{Per-source vectors
(recycled to \eqn{K}) forwarded to \code{\link{ABCpp.SingleInMultiple}}.}

\item{gr}{Unused; kept for compatibility.}

\item{numsim}{Iterations per source.}

\item{NC}{Base-cluster count per source (\code{NULL} = data-driven).}

\item{NC2}{Optional number of clusters to cut the final tree into.}

\item{StopRange}{Passed to \code{\link{WeightedClust}}.}

\item{wc_weight}{Weight grid for \code{\link{WeightedClust}}.}

\item{wc_weightclust}{Focus weight whose result is returned in \code{Clust}.}

\item{wc_linkage, wc_alpha}{Final clustering controls for \code{\link{WeightedClust}}.}

\item{mds}{Logical; MDS of the consensus dissimilarities.}

\item{nugget_type, nugget_args}{Forwarded to the nugget weighting.}
}
\value{
A list of class \code{"M_ABCdist_WC"} with \code{source_D}, the full
\code{WC} object, the focus \code{DistM} and \code{Clust}, and the
\code{weight_grid}.
}
\description{
Runs \code{\link{ABCdist.SingleInMultiple}} on each source, then feeds the
per-source dissimilarities to \code{\link{WeightedClust}} (\code{type =
"dist"}) to explore convex combinations over a weight grid and return the
focus-weight clustering.
}
\examples{
data(mosaic_toy)
fit <- M_ABCdist.WC(mosaic_toy$List, numsim = 40,
                    wc_weight = seq(1, 0, -0.25), wc_weightclust = 0.5)
dim(fit$DistM)
}
\references{
\insertCite{Amaratunga2008}{MosaiClusteR}
}
\seealso{
\code{\link{M_ABCdist}}, \code{\link{WeightedClust}}
}
