MCResultResampling.plotBootstrapT

Plot distribution of bootstrap pivot T for slope and intercept and compare
them with t(n-2) distribution.

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.plotBootstrapT function

<dl><dt>.Object</dt>
<dd>Object of class "MCResultResampling".</dd>
<dt>breaks</dt>
<dd>Number of breaks in histogram.</dd>
<dt>...</dt>
<dd>further graphical parameters</dd></dl>

Arguments

Plot distribution of bootstrap pivot T — MCResultResampling.plotBootstrapT

<dl>

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


<dt>breaks</dt>
<dd>Number of breaks in histogram.</dd>


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

</dl>

Plot distribution of bootstrap pivot T

MCResultResampling.plotBootstrapT: Plot distribution of bootstrap pivot T

Description

Usage

Value

Arguments