run_simulation_study: Run a simulation study

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

Run a simulation study

Description

Run the simulation workflows used in the article and thesis.

Usage

run_simulation_study(
  dimensions = list(c(500L, 100L), c(500L, 20L), c(100L, 20L), c(80L, 25L), c(60L, 33L),
    c(40L, 50L), c(20L, 100L)),
  true_ncomp = c(2L, 4L, 6L),
  missing_props = seq(5, 50, 5),
  mechanisms = c("MCAR", "MAR"),
  reps = 1L,
  seed = NULL,
  max_ncomp = 8L,
  criteria = c("Q2-LOO", "Q2-10fold", "AIC", "AIC-DoF", "BIC", "BIC-DoF"),
  incomplete_methods = c("nipals_standard", "nipals_adaptative"),
  imputation_methods = c("mice", "knn", "svd"),
  folds = 10L,
  mar_y_bias = 0.8
)

Arguments

dimensions	List of ⁠(n, p)⁠ integer pairs.
true_ncomp	Vector of true component counts.
missing_props	Missing-data proportions as fractions or percentages.
mechanisms	Missing-data mechanisms.
reps	Number of replicates.
seed	Optional base random seed.
max_ncomp	Maximum number of extracted components.
criteria	Criteria evaluated on complete and imputed data.
incomplete_methods	Incomplete-data NIPALS workflows.
imputation_methods	Imputation methods.
folds	Number of folds used by "Q2-10fold".
mar_y_bias	MAR bias parameter passed to add_missingness().

Value

A data frame with one row per study run.

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