mrIML (version 2.2.0)

mrIMLperformance: Calculate general performance metrics of a mrIML model

Description

Summarizes the performance of a mrIML object created using mrIMLpredicts() in a way that allows for easy comparison of different models. For regression models, root mean squared error (RMSE) and R-squared are reported, while for classification models, area under the ROC curve (AUC), Matthews correlation coefficient (MCC), positive predictive value (PPV), specificity, and sensitivity are reported.

Usage

mrIMLperformance(mrIMLobj)

Value

A list with two slots:

• $model_performance: A tibble of commonly used metrics that can be used to compare model performance of classification models. Performance metrics are based on the test data defined during mrIMLpredicts().

• $global_performance_summary: A global performance metric: the average of a performance metric over all response models. MCC is used for classification models and RMSE for regression models.

Arguments

mrIMLobj

A list object created by mrIMLpredicts() containing multi-response models.

Examples

mrIML_rf <- mrIML::mrIML_bird_parasites_RF

perf <- mrIMLperformance(mrIML_rf )
perf[[1]]
perf[[2]]