select_ncomp: Select the number of PLS components

In missPLS: Methods and Reproducible Workflows for Partial Least Squares with Missing Data

Select the number of PLS components

Description

Select the number of components for complete, imputed, or incomplete-data PLS workflows.

Usage

select_ncomp(
  x,
  y,
  method = c("complete", "nipals_standard", "nipals_adaptative"),
  criterion = c("Q2-LOO", "Q2-10fold", "AIC", "AIC-DoF", "BIC", "BIC-DoF"),
  max_ncomp,
  seed = NULL,
  folds = 10L,
  threshold = 0.0975
)

Arguments

x	Predictor matrix, dataset object, or misspls_imputation object.
y	Numeric response vector. This may be omitted when x already contains a response.
method	Selection workflow: "complete", "nipals_standard", or "nipals_adaptative".
criterion	Selection criterion: "Q2-LOO", "Q2-10fold", "AIC", "AIC-DoF", "BIC", or "BIC-DoF".
max_ncomp	Maximum number of components to consider.
seed	Optional random seed used by the cross-validation and imputation aggregation steps.
folds	Number of cross-validation folds used by "Q2-10fold".
threshold	Threshold applied to Q2 criteria.

Value

A one-row data frame describing the selected component count.

Examples

sim <- simulate_pls_data(n = 25, p = 10, true_ncomp = 2, seed = 1)
select_ncomp(sim$x, sim$y, method = "complete", criterion = "AIC", max_ncomp = 4, seed = 2)

missPLS documentation built on April 30, 2026, 9:09 a.m.