% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ABCdist.R
\name{ABCdist.SingleInMultiple}
\alias{ABCdist.SingleInMultiple}
\title{Single-source ABC with distance accumulation}
\usage{
ABCdist.SingleInMultiple(
  data,
  distmeasure = "euclidean",
  weighting = "var",
  normalize = NULL,
  gr = c(),
  bag = TRUE,
  numsim = 1000,
  numvar = 100,
  linkage = "ward.D",
  NC = NULL,
  mds = FALSE,
  nfeat = NULL,
  nugget_type = "between",
  nugget_args = list()
)
}
\arguments{
\item{data}{Numeric matrix with \strong{objects (samples) in rows} and
features in columns (the MosaiClusteR convention; transposed internally to
the features-by-objects layout of the original engine).}

\item{distmeasure}{Distance measure passed to \code{\link{Distance}}.}

\item{weighting}{\code{"var"}, \code{"cv"}, \code{"nugget"} or equal.}

\item{normalize}{Optional normalisation (see \code{\link{Normalization}}).}

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

\item{bag}{Logical; bootstrap the objects each iteration (Step 2).}

\item{numsim}{Number of iterations \eqn{R}.}

\item{numvar}{Kept for backward compatibility; superseded by \code{nfeat}.}

\item{linkage}{Agglomeration method for \code{fastcluster::hclust}.}

\item{NC}{Base-cluster count (\code{NULL} = \eqn{\lfloor\sqrt{N}\rfloor}).}

\item{mds}{Unused placeholder.}

\item{nfeat}{Optional feature-subsample size. \code{NULL} (default) uses the
classic \eqn{g=\lfloor\sqrt{G}\rfloor}; a value \eqn{\ge 1} sets an absolute
count (raise it for low-dimensional sources where most features matter).}

\item{nugget_type, nugget_args}{Passed to the data-nugget weighting when
\code{weighting = "nugget"}.}
}
\value{
A list with \code{D} (\eqn{N \times N} dissimilarity), \code{NC} and
\code{ng} (per-iteration feature count); if \code{mds = TRUE} also
\code{mds_coords}.
}
\description{
A variant of \code{\link{ABCpp.SingleInMultiple}} that accumulates the mean
scaled pairwise distance between co-selected objects across iterations,
yielding a \code{[0, 1]} dissimilarity matrix that \code{\link{M_ABCdist}}
fuses across sources.
}
\examples{
data(mosaic_toy)
out <- ABCdist.SingleInMultiple(mosaic_toy$List[[1]], numsim = 40)
dim(out$D)
}
\references{
\insertCite{Amaratunga2008}{MosaiClusteR}
}
\seealso{
\code{\link{M_ABCdist}}, \code{\link{ABCpp.SingleInMultiple}}
}
