Merge Results for MANOVA Model

This function takes the computed -log(p-values) from `train_model.manova_model` for a single ROI/searchlight and formats it into the standard output tibble.

This function takes the computed coefficients/correlations/t-values from train_model.rsa_model for a single ROI/searchlight and formats it into the standard output tibble.

Aggregates results (scores) and calls evaluate_model. Vector RSA typically doesn't involve folds in the same way as classifiers, so this mainly formats the output of train_model for the specific ROI/sphere.

Usage

# S3 method for class 'manova_model'
merge_results(obj, result_set, indices, id, ...)

# S3 method for class 'mvpa_model'
merge_results(obj, result_set, indices, id, ...)

# S3 method for class 'binary_classification_result'
merge_results(obj, ...)

# S3 method for class 'regression_result'
merge_results(obj, ...)

# S3 method for class 'multiway_classification_result'
merge_results(obj, ...)

# S3 method for class 'regional_mvpa_result'
merge_results(obj, ...)

# S3 method for class 'rsa_model'
merge_results(obj, result_set, indices, id, ...)

# S3 method for class 'vector_rsa_model'
merge_results(obj, result_set, indices, id, ...)

# S3 method for class 'feature_rsa_model'
merge_results(obj, result_set, indices, id, ...)

Arguments

obj

The vector RSA model specification (contains nperm etc.).

result_set

A tibble from the processor. Expected to contain the output of `train_model.vector_rsa_model` (the scores vector) likely within `$result[[1]]`.

indices

Voxel indices for the current ROI/searchlight sphere.

id

Identifier for the current ROI/searchlight center.

...

Additional arguments.

Value

A tibble row with the formatted performance metrics for the ROI/sphere.

A merged regional_mvpa_result object.

A tibble row with the formatted "performance" metrics (coefficients/t-values/correlations) for the ROI/sphere.

A tibble row with the final performance metrics for the ROI/sphere.

Examples

# Normally called internally during ROI/searchlight processing
dset <- gen_sample_dataset(D = c(4, 4, 4), nobs = 24, nlevels = 3, blocks = 3)
mspec <- manova_model(dset$dataset, manova_design(~ Y, dset$design$train_design))

# A processor result holding -log(p) values for one ROI
result_set <- tibble::tibble(result = list(c(Y = 2.3)), error = FALSE,
                             error_message = "~")
merge_results(mspec, result_set, indices = 1:10, id = 1)
#> # A tibble: 1 × 6
#>   result indices    performance      id error error_message
#>   <list> <list>     <list>        <dbl> <lgl> <chr>        
#> 1 <NULL> <int [10]> <dbl [1 × 1]>     1 FALSE ~