Calculate general performance metrics of a mrIML model

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)

Arguments

mrIMLobj

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

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.

Examples

mrIML_rf <- mrIML::mrIML_bird_parasites_RF

perf <- mrIMLperformance(mrIML_rf )
perf[[1]]
#> # A tibble: 4 × 8
#>   response     model_name roc_AUC   mcc sensitivity   ppv specificity prevalence
#>   <chr>        <chr>        <dbl> <dbl>       <dbl> <dbl>       <dbl>      <dbl>
#> 1 Hzosteropis  rand_fore…   0.863 0.520       0.913 0.880      0.581      0.265 
#> 2 Hkillangoi   rand_fore…   0.763 0.166       0.982 0.855      0.0952     0.116 
#> 3 Plas         rand_fore…   0.927 0.640       0.951 0.890      0.636      0.196 
#> 4 Microfilaria rand_fore…   0.761 0.376       1     0.917      0.154      0.0980
perf[[2]]
#> [1] 0.4253448