Function reference • manyROC

The main functions for ROC analysis
`print(<as_str>)` `print(<roc_df>)` `print(<roc_opt_result>)` `print(<roc_info>)` `roc_analysis()`	Carry out the ROC analysis
`print(<manyroc_result>)` `roc_manyroc()`	Carry out the manyROC analysis
`roc_manyroc_cv()`	Carry out the manyROC analysis with cross-validation
`roc_predict()`	Predict outcome for new data
`roc_predict_performance_by_gr()`	Calculate perfornamce for each pair of groups
`roc_merge_manyroc_cv_results()`	Merge 2 lists with manyROC CV results
`sp_manyroc_with_cv()`	Do manyROC analysis with cross-validation for hyperSpec object
`print(<hide_it>)` `sp_manyroc_with_cv_by_variable()`	Do manyROC analysis with cross-validation for hyperSpec object for each variable
Management functions for ROC objects
`roc_get()` `roc_get_all_results()` `roc_cutoff()` `roc_tp()` `roc_fn()` `roc_fp()` `roc_tn()` `roc_sens()` `roc_spec()`	[!] Access elements of roc_result_list object
Functions for cross-validation (CVO) ojects
`cvo_create_folds()` `print(<cvo>)`	Create a cvo (cross-valitation object)
`cvo_get_inds()` `cvo_get_info()` `cvo_get_sample_size()` `cvo_get_seeds()` `cvo_count_folds()` `cvo_get_fold_names()`	Access information in a cvo object
`cvo_test_bs()`	[+] Test if data in folds is stratified and blocked
Functions for classification performance
`calculate_performance()`	Performance measures for two-class classification
`roc_extract_info()`	[!!!] Extract the main information necessary for prediction
`calculate_acc()` `roc_calculate_acc()` `calculate_bac()` `roc_calculate_bac()` `calculate_auc()` `roc_calculate_auc()` `calculate_npv()` `roc_calculate_npv()` `calculate_ppv()` `roc_calculate_ppv()` `calculate_sensitivity()` `roc_calculate_se()` `calculate_specificity()` `roc_calculate_sp()` `calculate_youdens_j()` `roc_youdens_j()` `calculate_kappa()` `roc_calculate_kappa()` `calculate_wkappa()` `roc_calculate_wkappa()`	[!!!] Performance measures
`measure_kappa()`	Cohen's kappa
`measure_wkappa()`	Weighted Cohen's kappa
Helper and utility functions
`add_class_label()` `remove_class_label()`	Manage S3 class labels
`get_var_values()`	Get vector of variable values
`parallelSetSeed()`	Set seeds for reproducible parallel computing with 'parallelMap' package
Datasets
`fluorescence`	Dataset of simulated fluorescence spectra
Other functions
`manyROC`	ManyROC -- tools for ROC analysis

Reference

The main functions for ROC analysis

Management functions for ROC objects

Functions for cross-validation (CVO) ojects

Functions for classification performance

Helper and utility functions

Datasets

Other functions