MCResultResampling.calcResponse

Calculate predicted values for given values of the reference-method.

Regression methods to quantify the relation between two measurement
methods are provided by this package. In particular it addresses regression
problems with errors in both variables and without repeated measurements. It
implements the CLSI recommendations (see J. A. Budd et al.
(2018, <https://clsi.org/standards/products/method-evaluation/documents/ep09/>)
for analytical method comparison and bias estimation using patient samples.
Furthermore, algorithms for Theil-Sen and equivariant Passing-Bablok estimators
are implemented, see F. Dufey (2020, <doi:10.1515/ijb-2019-0157>) and
J. Raymaekers and F. Dufey (2022, <arXiv:2202:08060>).
A comprehensive overview over the implemented methods and references can be found
in the manual pages "mcr-package" and "mcreg".

Sergej Potapov

Method Comparison Regression

Fabian Model

Andre Schuetzenmeister

Ekaterina Manuilova

Florian Dufey

Jakob Raymaekers

Venkatraman E. Seshan

Roche 

MCResultResampling.calcResponse function

<dl><dt>.Object</dt>
<dd>object of class 'MCResultResampling'</dd>
<dt>x.levels</dt>
<dd>numeric vector specifying values of the reference method for which prediction should be made</dd>
<dt>alpha</dt>
<dd>significance level for confidence intervals</dd>
<dt>bootcimeth</dt>
<dd>bootstrap confidence interval method to be used</dd></dl>

Arguments

Caluculate Response — MCResultResampling.calcResponse

<dl>

<dt>.Object</dt>
<dd>object of class 'MCResultResampling'</dd>


<dt>x.levels</dt>
<dd>numeric vector specifying values of the reference method for which prediction should be made</dd>


<dt>alpha</dt>
<dd>significance level for confidence intervals</dd>


<dt>bootcimeth</dt>
<dd>bootstrap confidence interval method to be used</dd>

</dl>

MCResultResampling.calcResponse: Caluculate Response

Description

Usage

Value

Arguments