% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/multivariance-functions.R
\name{find.cluster}
\alias{find.cluster}
\title{cluster detection}
\usage{
find.cluster(x, vec = 1:ncol(x), list.cdm = cdms(x, vec = vec),
  mem = as.numeric(1:max(vec)), cluster.to.vertex = 1:max(mem),
  vertex.to.cdm = 1:max(mem), previous.n.o.cdms = rep(0, max(mem)),
  all.multivariances = numeric(0),
  g = igraph::add.vertices(igraph::graph.empty(, directed = FALSE),
  max(mem), label = sapply(1:max(mem), function(r) paste(colnames(x,
  do.NULL = FALSE, prefix = "")[vec == r], collapse = ",")), shape =
  "circle"), fixed.rejection.level = NA, alpha = 0.05,
  p.adjust.method = "holm", verbose = TRUE, kvec = 2:max(mem))
}
\arguments{
\item{x}{matrix with the samples}

\item{vec}{vector, it indicates which columns are initially treated together as one sample}

\item{list.cdm}{list of centered distance matrices}

\item{mem}{numeric vector, its length is the number of vertices, its content is the number of the corresponding cluster for the current iteration, i.e., vertex \code{i} belongs to cluster \code{mem[i]}}

\item{cluster.to.vertex}{vector, contains the cluster to vertex relations, i.e., \code{cluster.to.vertex[i]} is the index of the vertex which represents cluster \code{i}}

\item{vertex.to.cdm}{vector, contains the vertex to centered distance matrix relations, i.e., \code{vertex.to.cdm[i]} is the index centered distance matrix in \code{list.cdm} which corresponds to vertex \code{i}}

\item{previous.n.o.cdms}{vector, number of centered distance matrices in the previous iteration (it is used to ensure that previously check tuples are not checked again)}

\item{all.multivariances}{vector, which contains all distance multivariances which have been calculated so far. Only used to finally return all distance multivariances which have been calculated.}

\item{g}{dependence structure graph
fixed.rejection.level = NA, alpha=0.05,method = "holm",explore = FALSE, verbose = TRUE, kvec = 2:max(mem)}

\item{fixed.rejection.level}{vector, if not \code{NA} the \code{fixed.rejection.level[k]} is used for the k-tuples, instead of a level derived from the significance level \code{alpha}}

\item{alpha}{numeric, significance level used for the (distribution-free) tests}

\item{p.adjust.method}{name of the method used to adjust the p-values for multiple testing, see \code{\link[stats]{p.adjust}} for all possible options.}

\item{verbose}{boolean, if \code{TRUE} details during the detection are printed and whenever a cluster is newly detected the (so far) detected dependence structure is plotted.}

\item{kvec}{vector, k-tuples are only checked for each k in \code{kvec}, i.e., for \code{kvec = 2:4} only 2,3 and 4-tuples would be check and then the algorithm stops.}
}
\description{
Performs the detection of dependence structures algorithm until a cluster is found. This function is the basic building block \code{\link{dependence.structure}}. Advanced users, might use it directly.
}
\details{
For further details see \code{\link{dependence.structure}}.
}