MCResultAnalytical.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 Clinical Laboratory Standard International (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>).
Further the robust M-Deming and MM-Deming (experimental) are available, see
G. Pioda (2021, <arXiv:2105:04628>).
A comprehensive overview over the implemented methods and references can be found
in the manual pages 'mcrPioda-package' and 'mcreg'.

Giorgio Pioda

mcrPioda

Method Comparison Regression - Mcr Fork for M- And MM-Deming
Regression

Sergej Potapov

Fabian Model

Andre Schuetzenmeister

Ekaterina Manuilova

Florian Dufey

Jakob Raymaekers

Venkatraman E. Seshan

Roche 

MCResultAnalytical.calcResponse function

<dl><dt>.Object</dt>
<dd>object of class 'MCResultAnalytical'</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></dl>

Arguments

Calculate Response — MCResultAnalytical.calcResponse

<dl>

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

</dl>

Calculate Response

MCResultAnalytical.calcResponse: Calculate Response

Description

Usage

Value

Arguments