MCResultJackknife.getRJIF

getRJIF

Calculate the value of relative jackknife function for each observation.

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 

MCResultJackknife.getRJIF function

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

Arguments

Relative Jackknife Influence Function — MCResultJackknife.getRJIF

<dl>

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

</dl>

MCResultJackknife.getRJIF: Relative Jackknife Influence Function

Description

Usage

Value

Arguments

References