optimize_clusters: Function to Optimize Clusters Based on the Calinski-Harabasz...

In pintervals: Model Agnostic Prediction Intervals

Function to Optimize Clusters Based on the Calinski-Harabasz Index

Description

Function to Optimize Clusters Based on the Calinski-Harabasz Index

Usage

optimize_clusters(
  ncs,
  class_vec,
  method = c("ks", "kmeans"),
  min_m = 2,
  max_m = NULL,
  ms = NULL,
  maxit = 100,
  q = seq(0.1, 0.9, by = 0.1)
)

Arguments

ncs	Vector of non-conformity scores
class_vec	Vector of class labels
method	Clustering method to use, either 'ks' for Kolmogorov-Smirnov or 'kmeans' for K-means clustering
min_m	Minimum number of clusters to consider
max_m	Maximum number of clusters to consider. If NULL, defaults to the number of unique classes minus one
ms	Vector of specific numbers of clusters to consider. If NULL, defaults to a sequence from min_m to max_m
maxit	Maximum number of iterations for the clustering algorithm
q	Quantiles to use for K-means clustering, default is a sequence from 0.1 to 0.9 in steps of 0.1

Value

A vector of cluster assignments, with attributes containing the clusters, coverage gaps, method used, number of clusters, and the Calinski-Harabasz index

pintervals documentation built on March 3, 2026, 5:06 p.m.