BMIselect (version 1.0.1)

sim_A: Simulate dataset A: Independent continuous covariates with MCAR/MAR missingness

Description

Generates a dataset for Scenario A used in Bayesian MI-LASSO benchmarking. Covariates are iid standard normal, with a fixed true coefficient vector, linear outcome, missingness imposed on specified columns under MCAR or MAR, and multiple imputations via predictive mean matching.

Usage

sim_A(
  n = 100,
  p = 20,
  type = "MAR",
  SNP = 1.5,
  low_missing = TRUE,
  n_imp = 5,
  seed = NULL
)

Value

A list with components:

data_O

A list of complete covariate matrix and outcomes before missingness.

data_mis

A list of covariate matrix and outcomes with missing values.

data_MI

A list of array of imputed covariates (n_imp × n × p) and a matrix of imputed outcomes (n_imp × n).

data_CC

A list of complete-case covariate matrix and outcomes.

important

Logical vector of true nonzero coefficient indices.

covmat

True covariance matrix used for X.

beta

True coefficient vector.

Arguments

n

Integer. Number of observations.

p

Integer. Number of covariates (columns). Takes values in {20, 40}.

type

Character. Missingness mechanism: "MCAR" or "MAR".

SNP

Numeric. Signal-to-noise ratio controlling error variance.

low_missing

Logical. If TRUE, use low missingness rates; if FALSE, higher missingness.

n_imp

Integer. Number of multiple imputations to generate.

seed

Integer or NULL. Random seed for reproducibility.

Examples

sim <- sim_A(n = 100, p = 20, type = "MAR", SNP = 1.5,
             low_missing = TRUE, n_imp = 5, seed = 123)
str(sim)