LOSO kernel grid search

Evaluates a named list of candidate design kernels using LOSO explained variance at a fixed rank.

Usage

dkge_cv_kernel_grid(
  B_list,
  X_list,
  K_grid,
  rank,
  Omega_list = NULL,
  ridge = 0,
  w_method = "mfa_sigma1",
  w_tau = 0.3
)

Arguments

B_list

List of qxP subject beta matrices.

X_list

List of Txq subject design matrices.

K_grid

Named list of candidate kernels.

rank

Rank used for evaluation.

Omega_list

Optional list of spatial weights.

ridge

Optional ridge parameter passed to [dkge_fit()].

w_method

Subject-level weighting scheme passed to [dkge_fit()].

w_tau

Shrinkage parameter toward equal weights passed to [dkge_fit()].

Value

List with the one-SE pick, best kernel, summary table, and raw scores.

Examples

# \donttest{
toy <- dkge_sim_toy(
  factors = list(cond = list(L = 3)),
  active_terms = "cond", S = 4, P = 15, snr = 5
)
q <- nrow(toy$K)
K_grid <- list(base = toy$K, identity = diag(q))
cv <- dkge_cv_kernel_grid(toy$B_list, toy$X_list, K_grid, rank = 1)
cv$pick
#> [1] "base"
# }