Package index • diagnosticSummary

Construction

Generate a dx object

dx(): Set options for diagnostic analysis

dx_cm(): Create a Confusion Matrix from Predictions and Truth

dx_compare(): Compare Multiple Classification Models

Metrics

Measures of binary classification tasks

dx_accuracy(): Calculate Accuracy

dx_auc(): Calculate Area Under the ROC Curve (AUC)

dx_auc_pr(): Calculate Area Under the Precision-Recall Curve (AUC-PR)

dx_balanced_accuracy(): Calculate Balanced Accuracy

dx_brier(): Calculate Brier Score

dx_cohens_kappa(): Calculate Cohen's Kappa

dx_detection_prevalence(): Calculate Detection Prevalence

dx_f1(): Calculate F1 Score with Confidence Intervals

dx_f2(): Calculate F2 Score with Confidence Intervals

dx_fbeta(): Calculate F-beta Score with Confidence Intervals

dx_fdr(): Calculate False Discovery Rate (FDR)

dx_fowlkes_mallows(): Calculate Fowlkes-Mallows Index

dx_g_mean(): Calculate G-mean

dx_lrt_neg(): Calculate Negative Likelihood Ratio

dx_lrt_pos(): Calculate Positive Likelihood Ratio

dx_markedness(): Calculate Markedness

dx_mcc(): Calculate Matthews Correlation Coefficient (MCC)

dx_nir(): Calculate No Information Rate (NIR)

dx_npv(): Calculate Negative Predictive Value (NPV)

dx_npv_prevalence(): Calculate Negative Predictive Value (NPV) at Target Prevalence

dx_odds_ratio(): Calculate Odds Ratio

dx_ppv_prevalence(): Calculate Positive Predictive Value (PPV) at Target Prevalence

dx_prevalence(): Calculate Prevalence

dx_fnr() dx_miss_rate(): Calculate False Negative Rate (FNR)

dx_fpr() dx_fall_out(): Calculate False Positive Rate (FPR)

dx_informedness() dx_youden_j(): Calculate Informedness

dx_ppv() dx_precision(): Calculate Positive Predictive Value (PPV, Precision)

dx_sensitivity() dx_recall() dx_tpr(): Calculate Sensitivity (True Positive Rate, Recall)

dx_specificity() dx_tnr(): Calculate Specificity (True Negative Rate)

Tests

Statistical tests

breslow_day_test(): Breslow-Day Test for Homogeneity of Odds Ratios

dx_chi_square(): Chi-Square Test for Independence in a 2x2 Table

dx_delong(): DeLong's Test for Comparing Two ROC Curves

dx_fishers_exact(): Fisher's Exact Test for Independence in a 2x2 Table

dx_g_test(): G-Test (Log-Likelihood Ratio Test) for Independence in 2x2 Table

dx_mcnemars(): McNemar's Chi-squared Test for Paired Proportions

dx_z_test(): Z-test for Comparing Two Proportions

Figures

Figures summarizing diagnostic statistics

dx_plot_calibration(): Plot Calibration Curve

dx_plot_cap(): Plot Cumulative Accuracy Profile (CAP) Curve

dx_plot_cm(): Plot Confusion Matrix with Metrics

dx_plot_cost(): Plot Cost Curve

dx_plot_decision_curve(): Plot Decision Curve

dx_plot_forest(): Create table with odds ratios displayed graphically

dx_plot_gain(): Plot Gain Chart

dx_plot_ks(): Plot Kolmogorov-Smirnov Curve

dx_plot_lift(): Plot Lift Curve

dx_plot_pr(): Plot Precision-Recall Curve

dx_plot_predictive_value(): Plot Predictive Values Against Prevalence

dx_plot_probabilities(): Plot Predicted Probabilities

dx_plot_roc(): Plot ROC Curve

dx_plot_rocs(): Plot ROC Curves for Multiple Models

dx_plot_thresholds(): Plot Diagnostic Measures across Thresholds

dx_plot_youden_j(): Plot Youden's J Index Curve

dx_edit_cell(): Edit a cell within an object returned from dx_forest

Utilities

Helper functions and S3 methods

as.data.frame(<dx>): Convert to a data frame

summary(<dx>): Summary

Data

Test data sets

dx_heart_failure: Heart attack outcomes and predictions