Cumulative gains chart

Shows how many of the positive cases are captured as a growing share of the population is targeted in order of predicted score. It is the customary chart for judging a classifier's value for ranking and targeting, as in marketing, triage and fraud detection. The diagonal marks the no-model baseline and the upper envelope a perfect model. Pass a named list of models / (actual, score) pairs to overlay several colour-coded gains curves with a legend.

Usage

gain_plot(
  x,
  score = NULL,
  colour = depictr_brand(),
  legend_inside = FALSE,
  title = NULL
)

Arguments

x

A binomial glm; the vector of observed outcomes (0/1, logical or a two-level factor with the positive class second); or a named list of models / (actual, score) pairs to overlay.

score

When x is an outcome vector, the matching scores or predicted probabilities (or a list of them for the multi-model case).

colour

Curve colour for the single-model case. Defaults to the depictr brand blue. Ignored when several models are overlaid.

legend_inside

When TRUE (and several models are overlaid), draw the legend inside the panel (in the bottom-right triangle the concave curve leaves empty) over a translucent background, instead of in a right-hand margin. Defaults to FALSE.

title

Plot title.

Value

A ggplot2::ggplot object.

Examples

# Targeting only pays off when the positive class is scarce, so the example
# uses the rare clinical-trial adverse event (about 10% positive).
gfit <- glm(adverse_event ~ biomarker + age + arm,
            data = clinical_trial, family = binomial)
gain_plot(gfit)


# Compare two models.
reduced <- glm(adverse_event ~ biomarker, data = clinical_trial,
               family = binomial)
gain_plot(list(Full = gfit, Reduced = reduced))