find.cluster: cluster detection

In multivariance: Measuring Multivariate Dependence Using Distance Multivariance

Description

Performs the detection of dependence structures algorithm until a cluster is found. This function is the basic building block dependence.structure. Advanced users, might use it directly.

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),
  parameter.range = NULL,
  type = "conservative",
  stop.too.many = NULL,
  ...
)

Arguments

x	matrix with the samples
vec	vector, it indicates which columns are initially treated together as one sample
list.cdm	list of doubly centered distance matrices
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 i belongs to cluster mem[i]
cluster.to.vertex	vector, contains the cluster to vertex relations, i.e., cluster.to.vertex[i] is the index of the vertex which represents cluster i
vertex.to.cdm	vector, contains the vertex to doubly centered distance matrix relations, i.e., vertex.to.cdm[i] is the index of the doubly centered distance matrix in list.cdm which corresponds to vertex i
previous.n.o.cdms	vector, number of the doubly centered distance matrices in the previous iteration (it is used to ensure that previously check tuples are not checked again)
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.
g	dependence structure graph
fixed.rejection.level	vector, if not NA the fixed.rejection.level[k] is used for the k-tuples, instead of a level derived from the significance level alpha
alpha	numeric, significance level used for the (distribution-free) tests
p.adjust.method	name of the method used to adjust the p-values for multiple testing, see p.adjust for all possible options.
verbose	boolean, if TRUE details during the detection are printed and whenever a cluster is newly detected the (so far) detected dependence structure is plotted.
kvec	vector, k-tuples are only checked for each k in kvec, i.e., for kvec = 2:4 only 2,3 and 4-tuples would be check and then the algorithm stops.
parameter.range	numeric matrix, which hosts the range of significance levels or 'c.factor' which yield the same detected structure
type	the method for the detection, one of 'conservative','resample','pearson_approx' or 'consistent'.
stop.too.many	numeric, upper limit for the number of tested tuples. A warning is issued if it is used. Use stop.too.many = NULL for no limit.
...	are passed to resample.multivariance in the case of 'type = resample'

Details

For further details see dependence.structure.

multivariance documentation built on Oct. 6, 2021, 5:08 p.m.