Plot Calibration Curve

Generates a calibration plot to assess the calibration of predicted probabilities against observed outcomes. The function plots the mean predicted probability against the observed proportion of positive outcomes for different bins of predicted probabilities.

Usage

dx_plot_calibration(dx_obj, bins = 10)

Arguments

dx_obj

A list or object containing the model's predicted probabilities (predicted_probs) and the actual binary outcomes (true_labels).

bins

An integer specifying the number of bins to divide the predicted probabilities into. Default is 10 (deciles).

Value

A ggplot object representing the calibration curve, which can be further customized as needed.

Details

The calibration curve is an important diagnostic tool to understand how well a model's predicted probabilities match the observed frequencies of the outcomes. A well-calibrated model will have a calibration curve that closely follows the diagonal line y = x, indicating that the predicted probabilities are reflective of the true likelihood of the event. The curve is constructed by dividing the predicted probabilities into bins number of bins and then plotting the average predicted probability against the observed proportion of positive outcomes in each bin. The number of bins can be adjusted to change the granularity of the calibration curve.

A calibration curve that deviates significantly from the diagonal line indicates that the model may be under or overestimating the probabilities. In such cases, calibration methods like isotonic regression or Platt scaling might be applied to adjust the model's predicted probabilities.

Examples

dx_obj <- dx(
  data = dx_heart_failure,
  true_varname = "truth",
  pred_varname = "predicted",
  outcome_label = "Heart Attack",
  setthreshold = .3
)
dx_plot_calibration(dx_obj)