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Plot a ROC curve

Source: R/plotROC.R
plotROC.Rd

Sensitivity against the false positive rate across every cutoff, with the area under the curve reported. The diagonal is the reference: a model that ranks presences no better than chance lies on it.

Usage

plotROC(
  model,
  newdata = NULL,
  folds = NULL,
  title = "",
  show.auc = TRUE,
  theme = theme_fancyfx(),
  palette = fancyfx_palette(),
  linewidth = 0.8,
  ...
)

Arguments

model

A fitted presence/absence model.

newdata

Data to evaluate on. Required, and it should not be the data the model was fitted to – see threshold_metrics().

folds

Optional fold identifiers, one per row of newdata. Each fold is drawn as its own curve, so the spread is visible rather than averaged.

title

Plot title, optional.

show.auc

Whether to report the AUC on the plot.

theme

A ggplot2 theme. Defaults to theme_fancyfx().

palette

Colours used for fold curves. Defaults to fancyfx_palette().

linewidth

Width of the curve.

...

Passed to threshold_metrics() and on to stats::predict().

Value

A ggplot2 object.

Details

The plot is square with equal axes, because a ROC curve read on unequal axes misleads about how far from the diagonal it sits.

AUC is computed from ranks, not by integrating the drawn curve, so tied predictions are handled exactly. With folds, one AUC per fold is reported as a range rather than a mean: the spread is the informative part.

If the metrics came from the model's own training data, the plot says so beneath the axis. That annotation is not decoration – an in-sample ROC can look excellent for a model with no predictive value at all.

References

Allouche, O., Tsoar, A., & Kadmon, R. (2006). Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). Journal of Applied Ecology, 43(6), 1223-1232. doi:10.1111/j.1365-2664.2006.01214.x

See also

plotThreshold() for choosing a cutoff, threshold_metrics() for the numbers underneath.

Other evaluation plots: calc_deviance(), calibration_estimates(), held_out(), permutation_importance(), plotCalibration(), plotImportance(), plotThreshold(), spatial_sorting_bias(), threshold_metrics()

Examples

set.seed(1)
dat <- data.frame(x1 = runif(400, 1, 10), x2 = runif(400, 1, 10))
dat$y <- rbinom(400, 1, plogis(-3 + 0.6 * dat$x1))
train <- dat[1:200, ]
test <- dat[201:400, ]

fit <- glm(y ~ x1 + x2, data = train, family = binomial)
plotROC(fit, test)