cutpointr includes several convenience functions for
plotting data from a cutpointr object. These include:
plot_cutpointr: General purpose plotting function for
cutpointr or roc_cutpointr objectsplot_cut_boot: Plot the bootstrapped distribution of
optimal cutpointsplot_metric: If maximize_metric or
minimize_metric was used this function plots all possible
cutoffs on the x-axis vs. the respective metric values on the y-axis. If
bootstrapping was run, a confidence interval based on the bootstrapped
distribution of metric values at each cutpoint can be displayed. To
display no confidence interval set conf_lvl = 0.plot_metric_boot: Plot the distribution of out-of-bag
metric valuesplot_precision_recall: Plot the precision recall
curveplot_sensitivity_specificity: Plot all cutpoints
vs. sensitivity and specificityplot_roc: Plot the ROC curveplot_x: Plot the distribution of the predictor
variablelibrary(cutpointr)
set.seed(123)
opt_cut_b_g <- cutpointr(suicide, dsi, suicide, gender, boot_runs = 500)## Assuming the positive class is yes## Assuming the positive class has higher x values## Running bootstrap...## Warning: Removed 1 row containing non-finite outside the scale range
## (`stat_density()`).
## Warning: Removed 8 rows containing non-finite outside the scale range
## (`stat_density()`).
All plot functions, except for the standard plot method that returns
a composed plot, return ggplot objects than can be further
modified. For example, changing labels, title, and the theme can be
achieved this way:
library(ggplot2)
p <- plot_x(opt_cut_b_g)
p + ggtitle("Distribution of dsi") +
theme_minimal() +
xlab("Depression score")
Using plot_cutpointr any metric can be chosen to be
plotted on the x- or y-axis and results of cutpointr() as
well as roc() can be plotted. If a cutpointr
object is to be plotted, it is thus irrelevant which metric
function was chosen for cutpoint estimation. Any metric that can be
calculated based on the ROC curve can be subsequently plotted as only
the true / false positives / negatives over all cutpoints are needed.
That way, not only the above plots can be produced, but also any
combination of two metrics (or metric functions) and / or cutpoints. The
built-in metric functions as well as user-defined functions or anonymous
functions can be supplied to xvar and yvar. If
bootstrapping was run, confidence intervals can be plotted around the
y-variable. This is especially useful if the cutpoints, available in the
cutpoints function, are placed on the x-axis. Note that
confidence intervals can only be correctly plotted if the values of
xvar are constant across bootstrap samples. For example,
confidence intervals for TPR by FPR (a ROC curve) cannot be plotted
easily, as the values of the false positive rate vary per bootstrap
sample.
## Assuming the positive class is yes## Assuming the positive class has higher x values## Running bootstrap...
Since cutpointr returns a data.frame with
the original data, bootstrap results, and the ROC curve in nested
tibbles, these data can be conveniently extracted and plotted manually.
The relevant nested tibbles are in the columns data,
roc_curve and boot. The following is an
example of accessing and plotting the grouped data.
library(dplyr)
library(tidyr)
opt_cut_b_g |>
select(data, subgroup) |>
unnest(cols = data) |>
ggplot(aes(x = suicide, y = dsi)) +
geom_boxplot(alpha = 0.3) +
facet_grid(~subgroup)