BestC: Find Optimal Number of Clusters for Longitudinal Data

In CKNNRLD: Clustering-Based K-Nearest Neighbor Regression for Longitudinal Data

Find Optimal Number of Clusters for Longitudinal Data

Description

This function determines the best number of clusters (C) for longitudinal data clustering based on internal validation indices using the latrend package.

Usage

BestC(Y, range_clusters = 2:6, method = "kml")

Arguments

Y	A matrix or data frame of longitudinal outcomes (subjects x timepoints).
range_clusters	A numeric vector of cluster numbers to evaluate (e.g., 2:6).
method	Clustering method to use. Currently supports "kml" (default).

Value

A list with:

best_c	Optimal number of clusters
criteria	Data frame of criteria values for each cluster number
criteria_best	Criteria values for the best cluster number

Examples

set.seed(123)
n <- 30
T <- 3
y <- matrix(rnorm(n * T), nrow = n)
best_c_info <- BestC(Y = y, range_clusters = 2:4)
print(best_c_info$best_c)

CKNNRLD documentation built on May 29, 2026, 1:06 a.m.