hierclust: Hierarchical Clustering

In mkampert/rCOSA: Clustering Objects on Subsets of Attributes (COSA)

Description

Performs a hierarchical cluster analysis on a set of dissimilarities using the R procedure hclust and dendrogram.

Usage

hierclust(
  D,
  main = "Hierarchical Clustering",
  leaflab = c("perpendicular", "textlike", "none")[3],
  cex.main = NULL,
  denplot = TRUE,
  sub = "",
  xlab = "",
  ylab = "",
  method = "average",
  groupnr = NULL,
  colv = palette()[c(8, 2, 4, 3, 5, 6, 7, 1)],
  ...
)

Arguments

D	dissimilarities/distances of class dist.
main	plot title
leaflab	labels of the leaves, default is set to 'none'
cex.main	redundant argument
denplot	logical, whether to plot dendrogram. Default is set to TRUE.
sub	subtitle
xlab	label horizontal axis
ylab	label vertical axis
method	the agglomeration method to be used. This should be (an unambiguous abbreviation of) one of "ward", "single", "complete", "average", "mcquitty", "median" or "centroid".
groupnr	when known, each object can be given a number to which group it belongs
colv	vector containing color names for each group number
...	arguments which can be parsed to either the hclust and dendrogram procedures.

Value

Returns an invisible object of class dendrogram. If denplot is set to TRUE then the dendrogram is also plotted, and when the groups are known one can ask for a colored dendrogram.

mkampert/rCOSA documentation built on Dec. 23, 2019, 8:21 p.m.