rtichoke

rtichoke provides interactive and static visualizations for evaluating binary prediction models. It allows data scientists and clinical researchers to seamlessly analyze performance metrics, including:

• Discrimination: Receiver Operating Characteristic (ROC), Precision-Recall (PR), Gains, and Lift curves.
• Calibration: Calibration curves with decile and smooth (lowess) representations.
• Clinical Utility: Decision Curves and Interventions Avoided.
• Performance Tables & Reports: Interactive metric summaries and complete HTML reports.

For deep methodological intuition and articles, visit the rtichoke blog! For package guides and API reference, explore the pkgdown documentation site.

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Installation

You can install rtichoke from GitHub:

# install.packages("devtools")
devtools::install_github("uriahf/rtichoke")

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The rtichoke Mental Model

rtichoke is model-agnostic: it operates directly on predicted probabilities (probs) and observed binary outcomes (reals).

The core workflow follows a simple conceptual pipeline:

$$\text{Predicted Probabilities} + \text{Binary Outcomes} \longrightarrow \text{Prepare Performance Data} \longrightarrow \text{Visualize or Summarize}$$

You can either pass lists of probs and reals directly to one-step functions (create_*_curve) or pre-compute performance data using prepare_performance_data() and pass it to plotting/table rendering functions (plot_*_curve, render_performance_table).

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Quickstart Examples

All examples use the built-in benchmark dataset rtichoke::example_dat.

library(rtichoke)

1. Single Model

Pass predicted probabilities and observed binary outcomes as single-element lists:

create_roc_curve(
  probs = list(example_dat$estimated_probabilities),
  reals = list(example_dat$outcome)
)

2. Model Comparison

Compare multiple models evaluated on the same population by passing a named list of prediction vectors:

create_roc_curve(
  probs = list(
    "Good Model"   = example_dat$estimated_probabilities,
    "Bad Model"    = example_dat$bad_model,
    "Random Guess" = example_dat$random_guess
  ),
  reals = list(example_dat$outcome)
)

3. Population Comparison (e.g., Train / Test Split)

Compare performance across distinct cohorts (such as Train vs. Test sets):

train_df <- example_dat[example_dat$type_of_set == "train", ]
test_df  <- example_dat[example_dat$type_of_set == "test", ]

create_roc_curve(
  probs = list(
    "Train" = train_df$estimated_probabilities,
    "Test"  = test_df$estimated_probabilities
  ),
  reals = list(
    "Train" = train_df$outcome,
    "Test"  = test_df$outcome
  )
)

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Two-Step Workflow with Prepared Performance Data

For iterative plotting or performance tables, prepare performance data first:

perf_data <- prepare_performance_data(
  probs = list(
    "Good Model" = example_dat$estimated_probabilities,
    "Bad Model"  = example_dat$bad_model
  ),
  reals = list(example_dat$outcome)
)

# Plot ROC curve from prepared data
plot_roc_curve(perf_data)

# Render interactive performance table
render_performance_table(perf_data)

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Comprehensive Summary Report

Generate a single self-contained HTML report containing all supported visualizations and performance tables:

create_summary_report(
  probs = list("Primary Model" = example_dat$estimated_probabilities),
  reals = list(example_dat$outcome),
  file_path = "model_performance_report.html"
)

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Documentation & Resources

• Package Website & Guides: Comprehensive task-oriented tutorials (Discrimination, Calibration, Clinical Utility, Performance Tables).
• Recipes & Workflows: Quick copy-paste cheatsheet for common evaluation tasks.
• rtichoke Blog: Deep methodological insights, statistical derivations, and background theory.

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Getting Help

If you encounter a bug or have a feature request, please file an issue on GitHub Issues with a reproducible example.