Construct a Global MVPA Result

Usage

global_mvpa_result(
  performance_table,
  result,
  importance_map,
  importance_vector,
  activation_patterns,
  raw_weights,
  fold_fits,
  model_spec
)

Arguments

performance_table: Tibble of cross-validated performance metrics.
result: The merged classification/regression result object.
importance_map: Spatial object (NeuroVol/NeuroSurface) of per-feature importance.
importance_vector: Numeric vector of per-feature importance.
activation_patterns: The P x D activation pattern matrix A.
raw_weights: The averaged P x D weight matrix W.
fold_fits: Optional list of per-fold model_fit objects.
model_spec: The input model specification.

Value

An S3 object of class global_mvpa_result.

Examples

# Typically created by run_global(), shown here with toy values
result <- global_mvpa_result(
  performance_table = tibble::tibble(Accuracy = 0.8),
  result = NULL,
  importance_map = NULL,
  importance_vector = c(0.1, 0.2),
  activation_patterns = NULL,
  raw_weights = NULL,
  fold_fits = NULL,
  model_spec = list()
)
print(result)
#> 
#>   Global MVPA Result
#> 
#>   Performance:
#> # A tibble: 1 × 1
#>   Accuracy
#>      <dbl>
#> 1      0.8
#> 
#>   Importance vector: length 2 
#>     range: [ 0.1 , 0.2 ]
#>