ciROCbin

An object of class <code>rocit</code>, (<code>method = "binormal"</code>).

rocit_bin

Desired level of confidence to be estimated.

level

Number of bootstrap samples, used to estimate <code>var(A)</code>,
<code>var(B)</code>, <code>cov(A,B)</code>. See <code><a rd-options="" href="/link/ciROC.rocit?package=ROCit&version=2.1.1" data-mini-rdoc="ROCit::ciROC.rocit">ciROC.rocit</a></code>.

nboot

Function <code>ciROCbin</code> estimates confidence interval
of binormally estimated ROC curve.

Sensitivity (or recall or true positive rate), false positive rate, specificity, precision (or positive predictive value), negative predictive value, misclassification rate, accuracy, F-score- these are popular metrics for assessing performance of binary classifier for certain threshold. These metrics are calculated at certain threshold values. Receiver operating characteristic (ROC) curve is a common tool for assessing overall diagnostic ability of the binary classifier. Unlike depending on a certain threshold, area under ROC curve (also known as AUC), is a summary statistic about how well a binary classifier performs overall for the classification task. ROCit package provides flexibility to easily evaluate threshold-bound metrics. Also, ROC curve, along with AUC, can be obtained using different methods, such as empirical, binormal and non-parametric. ROCit encompasses a wide variety of methods for constructing confidence interval of ROC curve and AUC. ROCit also features the option of constructing empirical gains table, which is a handy tool for direct marketing. The package offers options for commonly used visualization, such as, ROC curve, KS plot, lift plot. Along with in-built default graphics setting, there are rooms for manual tweak by providing the necessary values as function arguments. ROCit is a powerful tool offering a range of things, yet it is very easy to use.

Riaz Khan

ROCit

Performance Assessment of Binary Classifier with Visualization

Md Riaz Ahmed Khan

Thomas Brandenburger

ciROCbin function

<dl><dt>rocit_bin</dt>
<dd>An object of class <code>rocit</code>, (<code>method = "binormal"</code>).</dd>
<dt>level</dt>
<dd>Desired level of confidence to be estimated.</dd>
<dt>nboot</dt>
<dd>Number of bootstrap samples, used to estimate <code>var(A)</code>,
<code>var(B)</code>, <code>cov(A,B)</code>. See <code>ciROC.rocit</code>.</dd></dl>

Arguments

<code>ciROCbin</code> is used internally in <code>ciROC.rocit</code>
of ROCit.

Comment

Confidence Interval of Binormal ROC Curve — ciROCbin

<dl>

<dt>rocit_bin</dt>
<dd>An object of class <code>rocit</code>, (<code>method = "binormal"</code>).</dd>


<dt>level</dt>
<dd>Desired level of confidence to be estimated.</dd>


<dt>nboot</dt>
<dd>Number of bootstrap samples, used to estimate <code>var(A)</code>,
<code>var(B)</code>, <code>cov(A,B)</code>. See <code>ciROC.rocit</code>.</dd>

</dl>

<code>ciROCbin</code> is used internally in <code>ciROC.rocit</code>
of ROCit.

ciROCbin: Confidence Interval of Binormal ROC Curve

Description

Usage

Value

Arguments

Comment

See Also

Examples