rm_main_results: A result metric (RM) that calculates main decoding accuracy...
In emeyers/NDTr: The Neural Decoding Toolbox in R

Description Usage Arguments Details See Also Examples

This result metric calculate the zero-one loss, the normalized rank, and the mean of the decision values. This is also an S3 object which has an associated plot function to display the results.

rm_main_results(
  aggregate_decision_values = TRUE,
  aggregate_normalized_rank = TRUE
)

aggregate_decision_values

A string or boolean specifying how the decision values should be aggregated. If this is a boolean set to TRUE or to the string "full", then the decision values for the correct category will be calculated. If this is a boolean set to FALSE or to the string "none", then the decision values will not be calculated. If this is a string set to either "diag" or "only same train test time" then the decision values will only be calculated when for results when training and testing at the same time. Not returning the full results can speed up the runtime of the code and will use less memory so this can be useful for large data sets.

aggregate_normalized_rank

A string or boolean specifying how the normalized rank results should be aggregated. If this is a boolean set to TRUE or to the string "full", then the decision values for the correct category will be calculated. If this is a boolean set to FALSE or to the string "none", then the decision values will not be calculated. If this is a string set to either "diag" or "only same train test time" then the decision values will only be calculated when for results when training and testing at the same time. Not returning the full results can greatly speed up the runtime of the code and will use less memory so this can be useful for large data sets.

Like all result metrics, this result metric has functions to aggregregate results after completing each set of cross-validation classifications, and also after completing all the resample runs. The results should then be available in the DECODING_RESULTS object returned by the cross-validator.

Other result_metrics: plot.rm_confusion_matrix(), plot.rm_main_results(), rm_confusion_matrix()

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# If you only want to use the rm_main_results(), then you can put it in a
# list by itself and pass it to the cross-validator.
the_rms <- list(rm_main_results())