Classification metrics versus decision threshold

Sweeps the decision threshold across the full range of scores and plots the chosen classification metrics - any of sensitivity (= recall), specificity, precision and F1 - as colour-coded curves. This is the natural companion to the ROC and precision-recall curves for choosing an operating point: it shows directly how each metric trades off as the cut-off moves, and (by default) marks the Youden's J and maximum-F1 optimal thresholds.

Usage

threshold_plot(
  x,
  score = NULL,
  metrics = c("sensitivity", "specificity", "precision", "f1"),
  mark = c("youden", "f1"),
  title = NULL
)

Arguments

x

A binomial glm, or the vector of observed outcomes (0/1, logical or a two-level factor with the positive class second).

score

When x is an outcome vector, the matching scores or predicted probabilities.

metrics

Which metrics to draw: any subset of "sensitivity", "specificity", "precision" and "f1". ("recall" is accepted as an alias for "sensitivity".)

mark

Which optimal operating points to mark with a dashed vertical line: any subset of "youden" (max sensitivity + specificity - 1) and "f1" (max F1). Use character(0) or NULL to mark none.

title

Plot title.

Value

A ggplot2::ggplot object. The Youden and max-F1 thresholds are stored in attr(plot, "thresholds").

Details

At threshold \(t\) a case is predicted positive when its score is \(\ge t\). The metrics are evaluated at every distinct score (the points at which the confusion matrix can change), so the curves are exact step functions rather than a coarse grid.

Examples

gfit <- glm(accuracy ~ word_frequency + RT + condition,
            data = lexical_decision, family = binomial)
threshold_plot(gfit)


# Sensitivity / specificity trade-off only, no markers.
threshold_plot(gfit, metrics = c("sensitivity", "specificity"),
               mark = NULL)