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Generate Tree Structures with Hierarchical Clustering

Source: R/craft-align-hclust.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. Use NULL, if you don't want to calculate the distance.

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 accepts the calculated distance (or the input matrix if distance is NULL) and returns a hclust object. Alternative, you can supply an object which can be coerced to hclust.

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 
#>