Partitioning Around Medoids (PAM) clustering for harmonized data

This function performs Partitioning Around Medoids (PAM) clustering on the harmonized training data using specified distance measures (euclidean, pearson, or spearman). It adds the clustering results to the cluster.result slot of the precision object.

Usage

cluster.pam(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 PAM clustering results added to the cluster.result slot.