run_real_data_study: Run a real-data study

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

Run a real-data study

Description

Run a real-data study

Usage

run_real_data_study(
  dataset,
  seed = NULL,
  missing_props = seq(5, 50, 5),
  mechanisms = c("MCAR", "MAR"),
  reps = 1L,
  baseline_reps = 100L,
  max_ncomp = 12L,
  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

dataset	A packaged dataset name or misspls_dataset object.
seed	Optional base random seed.
missing_props	Missing-data proportions as fractions or percentages.
mechanisms	Missing-data mechanisms.
reps	Number of replicate missingness draws for each mechanism and proportion.
baseline_reps	Number of repeated complete-data ⁠Q2-10fold⁠ selections used to determine ⁠t**⁠.
max_ncomp	Maximum number of extracted components.
criteria	Criteria evaluated on incomplete 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.