plot_cutpointr {cutpointr} | R Documentation |
Flexibly plot various metrics against all cutpoints or any other metric.
The function can plot any metric based on a cutpointr
or roc_cutpointr
object. If cutpointr
was run with bootstrapping, bootstrapped confidence
intervals can be plotted. These represent the quantiles of the distribution
of the y-variable grouped by x-variable over all bootstrap repetitions.
plot_cutpointr( x, xvar = cutpoint, yvar = sum_sens_spec, conf_lvl = 0.95, aspect_ratio = NULL )
x |
A |
xvar |
A function, typically |
yvar |
A function, typically a metric function. |
conf_lvl |
(numeric) If bootstrapping was run and x is a cutpointr object, a confidence interval at the level of conf_lvl can be plotted. To plot no confidence interval set conf_lvl = 0. |
aspect_ratio |
(numeric) Set to 1 to obtain a quadratic plot, e.g. for plotting a ROC curve. |
The arguments to xvar
and yvar
should be metric functions. Any metric
function that is suitable for cutpointr
can also be used in plot_cutpointr
.
Anonymous functions are also allowed.
To plot all possible cutpoints, the utility function cutpoint
can be used.
The functions for xvar
and yvar
may accept any or all of the arguments
tp
, fp
, tn
, or fn
and return a numeric vector,
a matrix or a data.frame
.
For more details on metric functions see vignette("cutpointr")
.
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, as the values of the false positive
rate vary per bootstrap sample.
Other cutpointr plotting functions:
plot.cutpointr()
,
plot_cut_boot()
,
plot_metric_boot()
,
plot_metric()
,
plot_precision_recall()
,
plot_roc()
,
plot_sensitivity_specificity()
,
plot_x()
set.seed(1) oc <- cutpointr(suicide, dsi, suicide, boot_runs = 10) plot_cutpointr(oc, cutpoint, F1_score) ## ROC curve plot_cutpointr(oc, fpr, tpr, aspect_ratio = 1) ## Custom function plot_cutpointr(oc, cutpoint, function(tp, tn, fp, fn, ...) tp / fp) + ggplot2::ggtitle("Custom metric") + ggplot2::ylab("value")