AgnesParam-class: Agglomerative nesting

In LTLA/bluster: Clustering Algorithms for Bioconductor

Agglomerative nesting

Description

Run the agnes function on a distance matrix within clusterRows.

Usage

AgnesParam(
  metric = NULL,
  stand = NULL,
  method = NULL,
  par.method = NULL,
  cut.fun = NULL,
  cut.dynamic = FALSE,
  cut.params = list()
)

## S4 method for signature 'ANY,AgnesParam'
clusterRows(x, BLUSPARAM, full = FALSE)

Arguments

metric, stand, method, par.method	Further arguments to pass to agnes.
cut.fun	Function specifying the method to use to cut the dendrogram. The first argument of this function should be the output of hclust, and the return value should be an atomic vector specifying the cluster assignment for each observation. Defaults to cutree if cut.dynamic=FALSE and cutreeDynamic otherwise.
cut.dynamic	Logical scalar indicating whether a dynamic tree cut should be performed using the dynamicTreeCut package.
cut.params	Further arguments to pass to cut.fun.
x	A numeric matrix-like object where rows represent observations and columns represent variables.
BLUSPARAM	A HclustParam object.
full	Logical scalar indicating whether the hierarchical clustering statistics should be returned.

Details

To modify an existing AgnesParam object x, users can simply call x[[i]] or x[[i]] <- value where i is any argument used in the constructor.

If cut.fun=NULL, cut.dynamic=FALSE and cut.params does not have h or k, clusterRows will automatically set h to half the tree height when calling cutree.

Value

The AgnesParam constructor will return a AgnesParam object with the specified parameters.

The clusterRows method will return a factor of length equal to nrow(x) containing the cluster assignments. If full=TRUE, a list is returned with clusters (the factor, as above) and objects (a list containing agnes, the function output; dist, the dissimilarity matrix; and hclust, a hclust object created from agnes).

Author(s)

Aaron Lun

See Also

agnes, which actually does all the heavy lifting.

HclustParam, for the more commonly used implementation of hierarchical clustering.

Examples

clusterRows(iris[,1:4], AgnesParam())
clusterRows(iris[,1:4], AgnesParam(method="ward"))

LTLA/bluster documentation built on Aug. 20, 2023, 5:39 a.m.