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 = choose_theme(getOption("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. The convention used in the package is the second level is 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: Theme object.
filename: Character: Path to save output.
...: Additional arguments passed to draw_scatter

Value

plotly object.

Author

EDG

Examples

if (FALSE) { # interactive()
# Synthetic data with n cases
n <- 500L
true_labels <- factor(sample(c("A", "B"), n, replace = TRUE))
# Synthetic probabilities where A has mean 0.25 and B has mean 0.75
predicted_prob <- ifelse(true_labels == "A",
  rbeta(n, 2, 6),
  rbeta(n, 6, 2)
)
draw_calibration(true_labels, predicted_prob)
}