plot_metric

If <code>maximize_metric</code> is used as <code>method</code> function in cutpointr the computed
metric values over all possible cutoffs can be plotted. Generally, this
works for method functions that return a ROC-curve including the metric
value for every cutpoint along with the optimal cutpoint.

Estimate cutpoints that optimize a specified metric in binary classification tasks
and validate performance using bootstrapping. Some methods for more robust cutpoint
estimation are supported, e.g. a parametric method assuming normal distributions,
bootstrapped cutpoints, and smoothing of the metric values per cutpoint using
Generalized Additive Models. Various plotting functions are included. For an overview
of the package see Thiele and Hirschfeld (2021) <doi:10.18637/jss.v098.i11>.

Christian Thiele

cutpointr

Determine and Evaluate Optimal Cutpoints in Binary
Classification Tasks

plot_metric function

<dl><dt>x</dt>
<dd>A cutpointr object.</dd>
<dt>conf_lvl</dt>
<dd>The confidence level of the bootstrap confidence interval.
Set to 0 to draw no bootstrap confidence interval.</dd>
<dt>add_unsmoothed</dt>
<dd>Add the line of unsmoothed metric values to the plot.
Applicable for some smoothing methods, e.g. maximize_gam_metric.</dd></dl>

Arguments

Plot a metric over all possible cutoffs from a cutpointr object — plot_metric

<dl>

<dt>x</dt>
<dd>A cutpointr object.</dd>


<dt>conf_lvl</dt>
<dd>The confidence level of the bootstrap confidence interval.
Set to 0 to draw no bootstrap confidence interval.</dd>


<dt>add_unsmoothed</dt>
<dd>Add the line of unsmoothed metric values to the plot.
Applicable for some smoothing methods, e.g. maximize_gam_metric.</dd>

</dl>

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plot_metric: Plot a metric over all possible cutoffs from a cutpointr object

Description

Usage

Arguments

See Also

Examples