mc.calcLinnetCI

Calculate Jackknife confidence intervals for intercept, slope or bias
given of vector of jackknife point estimates and global point estimate.

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 

mc.calcLinnetCI function

<dl><dt>Xjack</dt>
<dd>vector of point estimates for jackknife samples. 
The i-th element contains point estimate for the dataset without the i-th observation.</dd>
<dt>xhat</dt>
<dd>point estimate for the complete data set (scalar).</dd>
<dt>alpha</dt>
<dd>numeric value specifying the 100(1-alpha)% confidence level for the confidence interval (Default is 0.05).</dd></dl>

Arguments

Jackknife Confidence Interval — mc.calcLinnetCI

<dl>

<dt>Xjack</dt>
<dd>vector of point estimates for jackknife samples. 
The i-th element contains point estimate for the dataset without the i-th observation.</dd>


<dt>xhat</dt>
<dd>point estimate for the complete data set (scalar).</dd>


<dt>alpha</dt>
<dd>numeric value specifying the 100(1-alpha)% confidence level for the confidence interval (Default is 0.05).</dd>

</dl>

mc.calcLinnetCI: Jackknife Confidence Interval

Description

Usage

Value

Arguments

References