calculate_roc

continuous marker values or predictions of class labels

class labels, must be coded as 0 and 1. If not numeric with 0/1,
then plotROC assumes the first level in sort order is healthy status, with
a warning.

Logical, if true, will calculate exact joint confidence regions for
the TPF and FPF

Confidence level, ignored if <code>ci = FALSE</code>

alpha

Most ROC curve plots obscure the cutoff values and inhibit
interpretation and comparison of multiple curves. This attempts to address
those shortcomings by providing plotting and interactive tools. Functions
are provided to generate an interactive ROC curve plot for web use, and
print versions. A Shiny application implementing the functions is also
included.

Michael Sachs

plotROC

Generate Useful ROC Curve Charts for Print and Interactive Use

Michael C. Sachs

Robert W. Corty

Luis Crouch 

calculate_roc function

<dl><dt>M</dt>
<dd>continuous marker values or predictions of class labels</dd>
<dt>D</dt>
<dd>class labels, must be coded as 0 and 1. If not numeric with 0/1,
then plotROC assumes the first level in sort order is healthy status, with
a warning.</dd>
<dt>ci</dt>
<dd>Logical, if true, will calculate exact joint confidence regions for
the TPF and FPF</dd>
<dt>alpha</dt>
<dd>Confidence level, ignored if <code>ci = FALSE</code></dd></dl>

Arguments

Calculate the Empirical ROC curve — calculate_roc

<dl>

<dt>M</dt>
<dd>continuous marker values or predictions of class labels</dd>


<dt>D</dt>
<dd>class labels, must be coded as 0 and 1. If not numeric with 0/1,
then plotROC assumes the first level in sort order is healthy status, with
a warning.</dd>


<dt>ci</dt>
<dd>Logical, if true, will calculate exact joint confidence regions for
the TPF and FPF</dd>


<dt>alpha</dt>
<dd>Confidence level, ignored if <code>ci = FALSE</code></dd>

</dl>

calculate_roc: Calculate the Empirical ROC curve

Description

Usage

Value

Arguments

Details