Hierarchical clustering analysis

hca(
  x,
  cor.method = scalop::cor.methods,
  dist.method = scalop::dist.methods,
  cluster.method = scalop::cluster.methods,
  max.dist = 1,
  h = NULL,
  k = NULL,
  min.size = 5,
  max.size = 0.5
)

hca_cor(
  x,
  return.steps = F,
  reorder = T,
  reorder.col = reorder,
  reorder.row = reorder,
  ...
)

hca_dist(x, return.steps = F, ...)

hca_tree(x, return.steps = F, ...)

hca_order(x, return.steps = F, cor.method = "pearson", ...)

hca_groups(x, return.steps = F, ...)

hca_reorder(x, col = T, row = T, cor.method = "none", ...)

Arguments

x: a matrix (features X observations), an object of class `dist` or an object of class `hclust`.
cor.method: option if <x> is a matrix. One of 'pearson' (default), 'kendall', 'spearman' or 'none' (no correlation coefficents computed). Default: scalop::cor.methods
dist.method: option if <x> is a matrix or correlation matrix. One of 'euclidean' (default), 'maximum', 'manhattan', 'canberra', 'binary', 'minkowski' or 'none' (in which case stats::as.dist() will be used). Default: scalop::dist.methods
cluster.method: one of "average" (default), "complete", "single", "ward.D", "ward.D2", "mcquitty", "median" or "centroid". Default: scalop::cluster.methods
max.dist: maximum distance between observations. Default: 1
h: height(s) at which to cut the tree. If NULL, h will be set to all tree heights. Default: NULL
k: number of groups to return from tree. If <h> and <k> are both not NULL, <k> takes precedence. Default: NULL
min.size: minimum allowed cluster/group size. Values between 0 and 1 are interpreted as fractions of total count. Groups smaller than <min.size> are filtered out. Default: 5
max.size: maximum allowed cluster/group size. Values between 0 and 1 are interpreted as fractions of total count. Groups larger than <max.size> are filtered.out. Default: 0.5

Value

list