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Generate Tree Structures with Hierarchical Clustering

Source: R/dendrogram.R
hclust2.Rd

Generate Tree Structures with Hierarchical Clustering

Usage

hclust2(
  matrix,
  distance = "euclidean",
  method = "complete",
  use_missing = "pairwise.complete.obs"
)

Arguments

matrix

A numeric matrix, or data frame.

distance

A string of distance measure to be used. This must be one of "euclidean", "maximum", "manhattan", "canberra", "binary" or "minkowski". Correlation coefficient can be also used, including "pearson", "spearman" or "kendall". In this way, 1 - cor will be used as the distance. In addition, you can also provide a dist object directly or a function return a dist object.

method

A string of the agglomeration method to be used. This should be (an unambiguous abbreviation of) one of "ward.D", "ward.D2", "single", "complete", "average" (= UPGMA), "mcquitty" (= WPGMA), "median" (= WPGMC) or "centroid" (= UPGMC). you can also provide a function which returns a hclust object.

use_missing

An optional character string giving a method for computing covariances in the presence of missing values. This must be (an abbreviation of) one of the strings "everything", "all.obs", "complete.obs", "na.or.complete", or "pairwise.complete.obs". Only used when distance is a correlation coefficient string.

Value

A hclust object.

See also

Examples

hclust2(dist(USArrests), method = "ward.D")
#> 
#> Call:
#> stats::hclust(d = d, method = method)
#> 
#> Cluster method   : ward.D 
#> Distance         : euclidean 
#> Number of objects: 50 
#>