MCResultResampling.plotBootstrapCoefficients

Plot distribution of bootstrap coefficients (slope and intercept).

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 

MCResultResampling.plotBootstrapCoefficients function

<dl><dt>.Object</dt>
<dd>Object of class "MCResultResampling"</dd>
<dt>breaks</dt>
<dd>see function 'hist' (?hist) for details</dd>
<dt>...</dt>
<dd>further graphical parameters</dd></dl>

Arguments

Plot distribution of bootstrap coefficients — MCResultResampling.plotBootstrapCoefficients

<dl>

<dt>.Object</dt>
<dd>Object of class "MCResultResampling"</dd>


<dt>breaks</dt>
<dd>see function 'hist' (?hist) for details</dd>


<dt>...</dt>
<dd>further graphical parameters</dd>

</dl>

Plot distribution of bootstrap coefficients

MCResultResampling.plotBootstrapCoefficients: Plot distribution of bootstrap coefficients

Description

Usage

Value

Arguments