computeSubResults

Helper function to compute the subscores for the distribution types and the mentioned categories

The provided benchmark suite enables the automated evaluation and comparison of any existing and novel indirect method for reference interval ('RI') estimation in a systematic way.
Indirect methods take routine measurements of diagnostic tests, containing pathological and non-pathological samples as input and use sophisticated
statistical methods to derive a model describing the distribution of the non-pathological samples, which can then be
used to derive reference intervals. The benchmark suite contains 5,760 simulated test sets with varying difficulty.
To include any indirect method, a custom wrapper function needs to be provided.
The package offers functions for generating the test sets, executing the indirect method and evaluating the results.
See ?RIbench or vignette("RIbench_package") for a more comprehensive description of the features.
A detailed description and application is described in Ammer T., Schuetzenmeister A., Prokosch H.-U., Zierk J., Rank C.M., Rauh M. "RIbench: A Proposed Benchmark for the Standardized Evaluation of Indirect Methods for Reference Interval Estimation". Clinical Chemistry (2022) <doi:10.1093/clinchem/hvac142>.

Tatjana Ammer

RIbench

Benchmark Suite for Indirect Methods for RI Estimation

Christopher Rank

Andre Schuetzenmeister

computeSubResults function

<dl><dt>errorDf</dt>
<dd>(data frame) containing the estimate reference intervals and all computed error measures</dd>
<dt>tableTCs</dt>
<dd>(data.frame) containing all information about the simulated test sets</dd>
<dt>distCat</dt>
<dd>(character) specifying the distribution category</dd>
<dt>errorParam</dt>
<dd>(character) specifiying for which error parameter the data frame should be generated</dd>
<dt>catList</dt>
<dd>(character) vector containing the categories to split the dataset</dd>
<dt>catLabels</dt>
<dd>(character) vector containing the labels that will be used for the categories</dd>
<dt>perfCombination</dt>
<dd>(character) specifying if mean (default), median or sum should be computed</dd></dl>

Arguments

Tatjana Ammer <a href="/link/tatjana.ammer%40roche.com?package=RIbench&version=1.0.2" data-mini-rdoc="RIbench::tatjana.ammer@roche.com">tatjana.ammer@roche.com</a>

Author

Helper function to compute the subscores for the distribution types and the mentioned categories — computeSubResults

<dl>

<dt>errorDf</dt>
<dd>(data frame) containing the estimate reference intervals and all computed error measures</dd>


<dt>tableTCs</dt>
<dd>(data.frame) containing all information about the simulated test sets</dd>


<dt>distCat</dt>
<dd>(character) specifying the distribution category</dd>


<dt>errorParam</dt>
<dd>(character) specifiying for which error parameter the data frame should be generated</dd>


<dt>catList</dt>
<dd>(character) vector containing the categories to split the dataset</dd>


<dt>catLabels</dt>
<dd>(character) vector containing the labels that will be used for the categories</dd>


<dt>perfCombination</dt>
<dd>(character) specifying if mean (default), median or sum should be computed</dd>

</dl>

Tatjana Ammer <a href='mailto:tatjana.ammer@roche.com'>tatjana.ammer@roche.com</a>

computeSubResults: Helper function to compute the subscores for the distribution types and the mentioned categories

Description

Usage

Value

Arguments

Author