Get multi-omics weights

Usage

get_multi_weights(
  mod_list,
  dat.list,
  y = NULL,
  weighted = FALSE,
  use_depth = FALSE,
  robust = FALSE,
  parallel = FALSE,
  normalized = FALSE,
  calc = "Both",
  ytry = 1,
  w = NULL,
  cores = NULL,
  seed = -5,
  ...
)

Arguments

mod_list: A named list of fitted random forest models.
dat.list: A named list of omics datasets used to align weights.
y: Optional response vector (reserved for extensions).
weighted: Logical; whether to use weighted importance updates.
use_depth: Logical; whether to average depth-aware importances.
robust: Logical; whether to use robust matrix-based aggregation. Requires calc = "Both".
parallel: Logical; whether to parallelize across models.
normalized: Logical; whether to normalize the merged weights. The default is FALSE, preserving raw forest IMD on its 0-to-1 scale for variable selection.
calc: Which importance side to compute: "X", "Y", or "Both".
ytry: Response sampling proportion used in node-level updates. Currently unused by the post-hoc tree traversal; reserved for future use.
w: Optional case weights. Currently unused by the post-hoc tree traversal; reserved for future use.
cores: Number of CPU cores used when parallel = TRUE; NULL (default) uses parallel::detectCores() - 1.
seed: Random seed passed to stochastic components. Currently unused by the post-hoc tree traversal; reserved for future use.
...: Additional arguments for downstream helper functions.