HCA: Hierarchical Cluster Analysis

In structToolbox: Data processing & analysis tools for Metabolomics and other omics

Description

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.

Details

This object makes use of functionality from the following packages:

• stats

Value

A HCA object.

References

R Core Team (2020). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.

Examples

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

structToolbox documentation built on Nov. 8, 2020, 6:54 p.m.