KNNRLD.tune: Tune k in KNNRLD using Cross-Validation

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

Tune k in KNNRLD using Cross-Validation

Description

Finds the optimal number of neighbors for KNN regression in longitudinal data using k-fold cross-validation. Evaluates k values from 2 to A.

Usage

KNNRLD.tune(
  y,
  x,
  nfolds = 10,
  folds = NULL,
  seed = NULL,
  A = 10,
  graph = FALSE
)

Arguments

y	Matrix of longitudinal outcomes (subjects x timepoints).
x	Matrix of predictor variables (subjects x features).
nfolds	Number of cross-validation folds (default = 10).
folds	Optional list of pre-specified fold indices. If provided, nfolds is ignored.
seed	Optional random seed for reproducibility.
A	Maximum number of neighbors to evaluate (searches from 2 to A, default = 10).
graph	Logical; if TRUE, plots MSPE vs. k.

Value

A list containing:

crit	Mean squared prediction error (MSPE) for each k value
best_k	Optimal number of neighbors (minimizes MSPE)
performance	Minimum MSPE value
runtime	Elapsed computation time

Examples

set.seed(123)
n <- 30
T <- 3
d <- 2
x <- matrix(runif(n * d), nrow = n)
y <- matrix(rnorm(n * T), nrow = n)
tune_result <- KNNRLD.tune(
  y = y,
  x = x,
  nfolds = 3,
  A = 4
)
str(tune_result)

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