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Learns a source->target mapping with grouped ridge domain adaptation and evaluates target-domain representational geometry (feature-RSA style) on held-out target folds. This model is intended for settings where source rows have higher SNR/coverage and target rows are the analysis domain of interest.

Usage

feature_rsa_da_model(
  dataset,
  design,
  mode = c("stacked", "coupled"),
  lambdas,
  alpha_recall = 0.2,
  alpha_target = NULL,
  rho = 5,
  recall_folds = NULL,
  target_folds = NULL,
  recall_nfolds = 5L,
  target_nfolds = NULL,
  recall_gap = 0L,
  target_gap = NULL,
  target_nperm = 0L,
  target_perm_strategy = c("circular_shift", "block_shuffle"),
  target_perm_block = NULL,
  rsa_simfun = c("spearman", "pearson"),
  return_diagnostics = FALSE,
  ...
)

Arguments

dataset

An `mvpa_dataset` with external `test_data`.

design

A `feature_sets_design` carrying `X_train` plus either fixed `X_test` predictors or a fold-aware `target_builder`.

mode

`"stacked"` or `"coupled"`.

lambdas

Named ridge penalties per feature set.

alpha_recall

Non-negative scalar weighting target-domain rows.

alpha_target

Optional alias for `alpha_recall`; if provided, overrides.

rho

Coupling strength for `mode = "coupled"`.

recall_folds

Optional explicit test-domain folds (list of `train`/`test` indices).

target_folds

Optional alias for `recall_folds`; if provided, overrides.

recall_nfolds

Number of contiguous folds for single-run targets.

target_nfolds

Optional alias for `recall_nfolds`; if provided, overrides.

recall_gap

Non-negative purge gap (TRs) for single-run contiguous folds.

target_gap

Optional alias for `recall_gap`; if provided, overrides.

target_nperm

Number of single-run target permutations for a null model.

target_perm_strategy

`"circular_shift"` or `"block_shuffle"` for target permutations.

target_perm_block

Optional block size (TRs) for `"block_shuffle"`.

rsa_simfun

Similarity for target RDM correlation, `"spearman"` or `"pearson"`.

return_diagnostics

If TRUE, store fold/parameter diagnostics in fits.

...

Additional fields stored on the model spec.

Value

A model spec of class `feature_rsa_da_model`.