getSubsetForDefinedCats

Function to group the data according to a specified feature.

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

getSubsetForDefinedCats function

<dl><dt>subsetDef</dt>
<dd>(character) listing either the analytes or distribution types for which the result files should be parsed</dd>
<dt>distType</dt>
<dd>(logical) indicating if 'subsetDef' refers to analytes (FALSE, default) or distribution types (TRUE)</dd>
<dt>tableTCs</dt>
<dd>(data.frame) containing all information about the simulated test sets</dd>
<dt>errorList</dt>
<dd>(list) containing the table with the computed error measurements</dd>
<dt>catList</dt>
<dd>(list) containing the categories to split the dataset</dd>
<dt>catLabels</dt>
<dd>(list) containing the labels that will be used for the categories</dd>
<dt>restrict</dt>
<dd>(character) indicating whether testcases should be filtered according to specified restriction, default NULL, 
e.g. fractionPathol &lt;= 0.30</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

Function to group the data according to a specified feature. — getSubsetForDefinedCats

<dl>

<dt>subsetDef</dt>
<dd>(character) listing either the analytes or distribution types for which the result files should be parsed</dd>


<dt>distType</dt>
<dd>(logical) indicating if 'subsetDef' refers to analytes (FALSE, default) or distribution types (TRUE)</dd>


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


<dt>errorList</dt>
<dd>(list) containing the table with the computed error measurements</dd>


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


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


<dt>restrict</dt>
<dd>(character) indicating whether testcases should be filtered according to specified restriction, default NULL, 
e.g. fractionPathol &lt;= 0.30</dd>

</dl>

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

getSubsetForDefinedCats: Function to group the data according to a specified feature.

Description

Usage

Value

Arguments

Author