Integrates ITEM-style trial-wise decoding into the `fit_roi` architecture by delegating trial-level estimation and covariance-aware decoding to `fmrilss`.
Usage
item_model(
dataset,
design,
mode = c("classification", "regression"),
metric = NULL,
ridge_u = 0,
ridge_w = 1e-04,
lsa_method = c("r", "cpp"),
solver = c("chol", "svd", "pinv"),
u_storage = c("matrix", "by_run"),
class_levels = NULL,
check_hash = FALSE,
return_predictions = TRUE,
compute_performance = TRUE,
...
)Arguments
- dataset
An `mvpa_dataset`.
- design
An `item_design` object.
- mode
Decoding mode: `"classification"` or `"regression"`.
- metric
Optional ITEM metric. Classification: `"accuracy"`, `"balanced_accuracy"`. Regression: `"correlation"`, `"rmse"`.
- ridge_u
Non-negative ridge used when computing `U`.
- ridge_w
Non-negative ridge used when fitting ITEM weights per fold.
- lsa_method
LS-A backend for `fmrilss::lsa()` (`"r"` or `"cpp"`).
- solver
Solver preference for ITEM linear solves (`"chol"`, `"svd"`, `"pinv"`).
- u_storage
Store trial covariance as full matrix (`"matrix"`) or run blocks (`"by_run"`).
- class_levels
Optional class order for classification.
- check_hash
Validate trial hash before CV when available.
- return_predictions
Keep trial-level prediction tables in ROI results.
- compute_performance
Reserved for API compatibility. ITEM always computes scalar ROI metrics for mapping.
- ...
Additional fields stored on the model spec.
Details
Conceptually, ITEM differs from the archived `hrfdecoder` pathway in where the decoder is fit: - ITEM (`item_model`) first estimates trial-wise responses (`Gamma`) via LS-A and then performs covariance-aware decoding on those trial estimates. - The archived `hrfdecoder` model fits a continuous-time decoder directly on TR-level data and aggregates TR predictions to events afterward.
Use ITEM when you want an explicit trial-estimation stage and direct control over trial covariance handling (`U`), especially for trial-level diagnostics.