rm_confusion_matrix: A result metric (RM) that calculates confusion matrices
In emeyers/NDTr: The Neural Decoding Toolbox in R

Description Usage Arguments Details See Also Examples

This result metric calculate a confusion matrices from all points in time.

rm_confusion_matrix(
  save_only_same_train_test_time = TRUE,
  create_decision_vals_confusion_matrix = TRUE
)

`save_only_same_train_test_time`	A boolean specifying whether one wants to save results to allow one to create the confusion matrices when training at one point in time and testing a different point in time. Setting this to FALSE can save memory.
`create_decision_vals_confusion_matrix`	A boolean specifying whether one wants to create a confusion matrix of the decision values. In this confusion matrix, each row corresponds to the correct class (like a regular confusion matrix) and each column corresponds to the mean decision value of the predictions for each class.

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_main_results()

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