Hierarchical clustering for harmonized data

This function performs hierarchical clustering on the harmonized training data using euclidean, pearson, or spearman distance measures. It adds the clustering results to the cluster.result slot.

Usage

cluster.hc(object, k = NULL, distance = "euclidean")

Arguments

object

A precision object containing harmonized training data in the slot harmon.train.data.

k

Integer. Number of clusters. If NULL or not given, uses the number of unique labels in training data.

distance

Distance measure to use for clustering. Options are "euclidean", "pearson", or "spearman". Default is "euclidean".

Value

Updated precision object with hierarchical clustering results added to the cluster.result slot.