mc.calcTstar

Compute Resampling T-statistic.
for Calculation of t-Bootstrap Confidence Intervals.

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 

mc.calcTstar function

<dl><dt>.Object</dt>
<dd>object of class "MCResultResampling".</dd>
<dt>x.levels</dt>
<dd>a numeric vector of clinical decision points of interest.</dd>
<dt>iter.max</dt>
<dd>maximal number of iterations for calculation of weighted Deming regression.</dd>
<dt>threshold</dt>
<dd>threshold for calculation of weighted Deming regression.</dd>
<dt>kM</dt>
<dd>Huber's k for the M weighting, default kM = 1.345</dd>
<dt>tauMM</dt>
<dd>Tukey's tau for bisquare redescending weighting function, default tauMM = 4,685</dd>
<dt>priorSlope</dt>
<dd>starting slope value for PiMMDeming, default priorSlope = 1</dd>
<dt>priorIntercept</dt>
<dd>starting intercept value for PiMMDeming, default priorIntercept = 0</dd>
<dt>bdPoint</dt>
<dd>Proportion of data points selected for the highly robust M regression used for the determination of the starting parameters. Default 0.5</dd></dl>

Arguments

Compute Resampling T-statistic. — mc.calcTstar

<dl>

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


<dt>x.levels</dt>
<dd>a numeric vector of clinical decision points of interest.</dd>


<dt>iter.max</dt>
<dd>maximal number of iterations for calculation of weighted Deming regression.</dd>


<dt>threshold</dt>
<dd>threshold for calculation of weighted Deming regression.</dd>


<dt>kM</dt>
<dd>Huber's k for the M weighting, default kM = 1.345</dd>


<dt>tauMM</dt>
<dd>Tukey's tau for bisquare redescending weighting function, default tauMM = 4,685</dd>


<dt>priorSlope</dt>
<dd>starting slope value for PiMMDeming, default priorSlope = 1</dd>


<dt>priorIntercept</dt>
<dd>starting intercept value for PiMMDeming, default priorIntercept = 0</dd>


<dt>bdPoint</dt>
<dd>Proportion of data points selected for the highly robust M regression used for the determination of the starting parameters. Default 0.5</dd>

</dl>

Compute Resampling T-statistic.

mc.calcTstar: Compute Resampling T-statistic.

Description

Usage

Value

Arguments

References