modCluster: Cluster Subjects

In exprso: Rapid Deployment of Machine Learning Algorithms

Description

This method clusters subjects based on feature data using any one of seven available clustering algorithms. See Arguments below.

Usage

modCluster(object, top = 0, how = "hclust", onlyCluster = FALSE, ...)

## S4 method for signature 'ExprsArray'
modCluster(object, top = 0, how = "hclust",
  onlyCluster = FALSE, ...)

Arguments

object	An ExprsArray object. The object containing the subject data to cluster.
top	A numeric scalar or character vector. A numeric scalar indicates the number of top features that should undergo feature selection. A character vector indicates specifically which features by name should undergo feature selection. Set top = 0 to include all features. A numeric vector can also be used to indicate specific features by location, similar to a character vector.
how	A character string. The name of the function used to cluster. Select from "hclust", "kmeans", "agnes", "clara", "diana", "fanny", or "pam".
onlyCluster	A logical scalar. Toggles whether to return a processed cluster object or an updated ExprsArray object.
...	Additional arguments to the cluster function and/or other functions used for clustering (e.g., dist and cutree).

Details

Note that this function will expect the argument k to define the returned number of clusters, except when how = "kmeans" in which case this function will expect the argument centers instead.

Value

Typically an ExprsArray object with subject cluster assignments added to the $cluster column of the @anot slot.

Methods (by class)

• ExprsArray: Method to compare ExprsArray objects.

exprso documentation built on May 1, 2019, 7:11 p.m.