Draw calibration plot

Usage

draw_calibration(
  true_labels,
  predicted_prob,
  n_bins = 10L,
  bin_method = c("quantile", "equidistant"),
  binclasspos = 2L,
  main = NULL,
  subtitle = NULL,
  xlab = "Mean predicted probability",
  ylab = "Empirical risk",
  show_marginal_x = TRUE,
  marginal_x_y = -0.02,
  marginal_col = NULL,
  marginal_size = 10,
  mode = "markers+lines",
  show_brier = TRUE,
  theme = rtemis_theme,
  filename = NULL,
  ...
)

Arguments

true_labels: Factor or list of factors with true class labels
predicted_prob: Numeric vector or list of numeric vectors with predicted probabilities
n_bins: Integer: Number of windows to split the data into
bin_method: Character: "quantile" or "equidistant": Method to bin the estimated probabilities.
binclasspos: Integer: Index of the positive class
main: Character: Main title
subtitle: Character: Subtitle, placed bottom right of plot
xlab: Character: x-axis label
ylab: Character: y-axis label
show_marginal_x: Logical: Add marginal plot of distribution of estimated probabilities
marginal_x_y: Numeric: y position of marginal plot
marginal_col: Character: Color of marginal plot
marginal_size: Numeric: Size of marginal plot
mode: Character: "lines", "markers", "lines+markers": How to plot.
show_brier: Logical: If TRUE, add Brier scores to trace names.
theme: Character or list: Theme to use for plot
filename: Character: Path to save output.
...: Additional arguments passed to draw_scatter

Value

A plotly object.

Author

EDG

Examples

if (FALSE) { # \dontrun{
data(segment_logistic, package = "probably")

# Plot the calibration curve of the original predictions
draw_calibration(
  true_labels = segment_logistic$Class,
  predicted_prob = segment_logistic$.pred_poor,
  n_bins = 10L,
  binclasspos = 2L
)

# Plot the calibration curve of the calibrated predictions
draw_calibration(
  true_labels = segment_logistic$Class,
  predicted_prob = calibrate(
    segment_logistic$Class,
    segment_logistic$.pred_poor
  )$fitted.values,
  n_bins = 10L,
  binclasspos = 2L
)
} # }