HCA {structToolbox} | R Documentation

## Hierarchical Cluster Analysis

### 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)

[Package structToolbox version 1.2.0 Index]