cancer

A dataset containing randomly generated measurements from three diagnostic classes as they may
arise in a cancer investigation.

datasets

Several statistical test functions as well as a function for exploratory data
analysis to investigate classifiers allocating individuals to one of three disjoint and
ordered classes. In a single classifier assessment the discriminatory power is compared
to classification by chance. In a comparison of two classifiers the null hypothesis
corresponds to equal discriminatory power of the two classifiers.
See also "ROC Analysis for Classification and Prediction in Practice" by Nakas, Bantis
and Gatsonis (2023), ISBN 9781482233704.

Reinhard Furrer

trinROC

Statistical Tests for Assessing Trinormal ROC Data

Samuel Noll

Benjamin Reiser

Christos T. Nakas

Annina Cincera

cancer function

A data frame with 100 rows and 10 variables (9 classifiers):<dl>
 <dt>trueClass</dt>
<dd>A factor, indicating the class membership of the
 individuals.</dd> <dt>Class1, Class2, Class3, Class4, Class5, Class6, Class7, Class8, Class9</dt>
<dd>Measurements of a specific classifier obtained from the patients that underwent the
 clinical study.</dd>
</dl>

Format

Synthetic data set to investigate three-class ROC data. — cancer

A data frame with 100 rows and 10 variables (9 classifiers):<dl>
 <dt>trueClass</dt>
<dd>A factor, indicating the class membership of the
 individuals.</dd>

 <dt>Class1, Class2, Class3, Class4, Class5, Class6, Class7, Class8, Class9</dt>
<dd>Measurements of a specific classifier obtained from the patients that underwent the
 clinical study.</dd>


</dl>

cancer: Synthetic data set to investigate three-class ROC data.

Description

Usage

Arguments

Format