Cumulative lift chart

Shows how many times more positive cases a classifier captures, at each depth of the score-ordered population, than random targeting would. A lift of 3 at the top 10% means that decile contains three times the baseline rate of positives. The horizontal line at 1 is the no-model baseline. Pass a named list of models / (actual, score) pairs to overlay several colour-coded lift curves with a legend.

Usage

lift_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 top-right corner, which the decaying lift 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

# Lift is measured against the base rate, 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)
lift_plot(gfit)


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