Full SSJGL workflow: CV selection + final fit with bootstrap CIs

Combines SSJGL_select_v0_cv and SSJGL_final_with_pcor_CI into a single call. First selects the best v0 via cross-validation, then fits the final model at that v0 and computes bootstrap confidence intervals for partial correlations.

Usage

SSJGL_CV_final_pcorCI(
  Y,
  v0s,
  folds = 5,
  B = 200,
  ci_level = 0.95,
  seed = 1,
  penalty = "fused",
  lambda0,
  lambda1,
  lambda2,
  v1 = 1,
  doubly = FALSE,
  rho = 1,
  a = 1,
  b = 1,
  maxitr.em.cv = 200,
  tol.em = 1e-04,
  maxitr.jgl.cv = 200,
  tol.jgl = 1e-05,
  maxitr.em = 500,
  maxitr.jgl = 500,
  truncate = 1e-05,
  normalize = FALSE,
  c = 0.1,
  impute = TRUE,
  verbose = TRUE
)

Arguments

Y

List of K data matrices.

v0s

Numeric vector of v0 values to search over.

folds

Integer number of CV folds. Default 5.

B

Integer number of bootstrap samples. Default 200.

ci_level

Numeric confidence level. Default 0.95.

seed

Integer random seed. Default 1.

penalty, lambda0, lambda1, lambda2, v1, doubly, rho, a, b

Arguments passed to ssjgl.

maxitr.em.cv, maxitr.jgl.cv

Max iterations for CV fits (smaller for speed). Defaults 200.

tol.em, tol.jgl

Convergence tolerances.

maxitr.em, maxitr.jgl

Max iterations for final fit. Defaults 500.

truncate, normalize, c, impute

Additional ssjgl arguments.

verbose

Logical. Default TRUE.

Value

A list with elements:

cv

Output of SSJGL_select_v0_cv.

final

Output of SSJGL_final_with_pcor_CI.

Examples

if (FALSE) { # \dontrun{
sim <- simulate_ssjgl_data(K = 2, p = 15, n = 100, seed = 42)
res <- SSJGL_CV_final_pcorCI(
  Y = sim$data_list,
  v0s = c(0.05, 0.01, 0.005),
  folds = 3, B = 20,
  penalty = "fused",
  lambda0 = 1, lambda1 = 0.5, lambda2 = 0.5,
  normalize = TRUE, impute = FALSE
)
res$cv$v0_best
res$final$pcor_hat[[1]][1:5, 1:5]
} # }