Multi-class ROC Analysis

Usage

multiclassroc(
  data,
  outcome,
  predictors,
  method = "ovr",
  calculate_macro_auc = TRUE,
  calculate_micro_auc = TRUE,
  calculate_weighted_auc = TRUE,
  confidence_intervals = TRUE,
  ci_method = "bootstrap",
  bootstrap_samples = 1000,
  confidence_level = 0.95,
  pairwise_comparisons = FALSE,
  confusion_matrix = TRUE,
  class_metrics = TRUE,
  plot_roc_curves = TRUE,
  plot_method = "overlay",
  plot_diagonal = TRUE,
  random_seed = 42
)

Arguments

data

the data as a data frame

outcome

a string naming the outcome variable (must have 3+ levels)

predictors

a vector of strings naming predictor variables (continuous scores)

method

method for multi-class ROC: 'ovr' (one class vs all others), 'ovo' (all pairwise comparisons), or 'multinomial' (global probability model)

calculate_macro_auc

calculate macro-average AUC across all classes (unweighted mean)

calculate_micro_auc

calculate micro-average AUC (aggregate all predictions)

calculate_weighted_auc

calculate weighted-average AUC (weighted by class prevalence)

confidence_intervals

calculate confidence intervals for AUC estimates

ci_method

method for confidence interval calculation

bootstrap_samples

number of bootstrap samples for CI calculation

confidence_level

confidence level for intervals (default: 0.95 for 95\

pairwise_comparisonsshow detailed results for all pairwise class comparisons (OvO method)

confusion_matrixshow confusion matrix at optimal global threshold

class_metricscalculate sensitivity, specificity, PPV, NPV for each class

plot_roc_curvesplot ROC curves for each class

plot_methoddisplay method for ROC curves

plot_diagonalshow diagonal reference line (random classifier)

random_seedrandom seed for reproducible bootstrap sampling

A results object containing:

results$instructionsText					a html
results$summaryTable					a table
results$classAucTable					a table
results$pairwiseTable					a table
results$classMetricsTable					a table
results$confusionMatrix					a table
results$rocPlot					an image
results$interpretationText					a html

Tables can be converted to data frames with asDF or as.data.frame. For example:results$summaryTable$asDFas.data.frame(results$summaryTable) Multi-class ROC Analysis for evaluating diagnostic performance with 3 or more diagnostic classes. This analysis extends traditional binary ROC to handle multi-category outcomes using one-vs-rest, one-vs-one, and global approaches. Common applications include tumor subtype classification, disease staging with multiple levels, and AI model validation for multi-class predictions. # Example with tumor subtype classification data <- data.frame( true_class = factor(sample(c("TypeA", "TypeB", "TypeC"), 100, replace=TRUE)), score_A = rnorm(100), score_B = rnorm(100), score_C = rnorm(100) )multiclassroc( data = data, outcome = 'true_class', predictors = c('score_A', 'score_B', 'score_C'), method = 'ovr', calculate_macro_auc = TRUE, confidence_intervals = TRUE )