rkt_ecdf

print.rkt_ecdf

mean.rkt_ecdf

variance.rkt_ecdf

plot.rkt_ecdf

Calculate an empirical cumulative distribution function based on a sample <code>x</code> and optionally a vector <code>w</code> of weights.

A set of functions for receiver operating characteristic (ROC) curve
estimation and area under the curve (AUC) calculation.
All functions are designed to work with aggregated data;
nevertheless, they can also handle raw samples.
In 'ROCket', we distinguish two types of ROC curve representations:
1) parametric curves - the true positive rate (TPR) and the false positive rate (FPR)
are functions of a parameter (the score),
2) functions - TPR is a function of FPR.
There are several ROC curve estimation methods available. An introduction to
the mathematical background of the implemented methods (and much more) can be found in
de Zea Bermudez, Gonçalves, Oliveira & Subtil (2014) and Cai & Pepe (2004).

Daniel Lazar

ROCket

Simple and Fast ROC Curves

rkt_ecdf function

<dl><dt>x</dt>
<dd>Numeric vector containing the sample. Alternatively, if <code>w</code> is supplied, distinct values within the sample. For S3 methods, a function of class <code>rkt_ecdf</code>.</dd>
<dt>w</dt>
<dd>Optional. Numeric vector containing the weights of each value in <code>x</code>.</dd>
<dt>...</dt>
<dd>Further parameters.</dd></dl>

Arguments

Empirical estimate of the CDF — rkt_ecdf

<dl>

<dt>x</dt>
<dd>Numeric vector containing the sample. Alternatively, if <code>w</code> is supplied, distinct values within the sample. For S3 methods, a function of class <code>rkt_ecdf</code>.</dd>


<dt>w</dt>
<dd>Optional. Numeric vector containing the weights of each value in <code>x</code>.</dd>


<dt>...</dt>
<dd>Further parameters.</dd>

</dl>

rkt_ecdf: Empirical estimate of the CDF

Description

Usage

Value

Arguments

Details

Examples