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Hierarchical Cluster Analysis

Source: R/hca_class.R
HCA.Rd

Hierarchical Cluster Analysis is a numerical technique that uses agglomerative clustering to identify clusters or groupings of samples.

Usage

HCA(
  dist_method = "euclidean",
  cluster_method = "complete",
  minkowski_power = 2,
  factor_name,
  ...
)

Arguments

dist_method

(character) Distance measure. Allowed values are limited to the following:

  • "euclidean": The euclidean distance (2 norm).

  • "maximum": The maximum distance.

  • "manhattan": The absolute distance (1 norm).

  • "canberra": A weighted version of the mahattan distance.

  • "minkowski": A generalisation of manhattan and euclidean distance to nth norm.

The default is "euclidean".

cluster_method

(character) Agglomeration method. Allowed values are limited to the following:

  • "ward.D": Ward clustering.

  • "ward.D2": Ward clustering using sqaured distances.

  • "single": Single linkage.

  • "complete": Complete linkage.

  • "average": Average linkage (UPGMA).

  • "mcquitty": McQuitty linkage (WPGMA).

  • "median": Median linkage (WPGMC).

  • "centroid": Centroid linkage (UPGMC).

The default is "complete".

minkowski_power

(numeric) The default is 2.

factor_name

(character) The name of a sample-meta column to use.

...

Additional slots and values passed to struct_class.

Value

A HCA object with the following output slots:

dist_matrix(dist) An object containing pairwise distance information between samples.
hclust(hclust) An object of class hclust which describes the tree produced by the clustering process.
factor_df(data.frame)

Details

This object makes use of functionality from the following packages:

  • stats

Inheritance

A HCA object inherits the following struct classes:

[HCA] >> [model] >> [struct_class]

References

R Core Team (2024). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.

Examples

M = HCA(
      dist_method = "euclidean",
      cluster_method = "complete",
      minkowski_power = numeric(0),
      factor_name = "V1")

D = iris_DatasetExperiment()
M = HCA(factor_name='Species')
M = model_apply(M,D)