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Description

Method Comparison Regression - Mcr Fork for M-.

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'.

mcrPioda is a fork of the mcr package with additional functionalities:

  • MDeming regression - With bootstrap CI and jackknife CI + SE
  • MMDeming regression - With bootstrap CI and jackknife CI + SE
  • NgMMDeming regression - With bootstrap CI and jackknife CI + SE
  • PiMMDeming regression - With bootstrap CI and jackknife CI + SE
  • plotBoxEllipses for Mahalanobis distance hypothesis testing

All regression functions are written in C out of the MM-Deming which is kept for reproducibility and should be considered deprecated.

There is an urgent need for M-Deming since all Passing Bablok regression are biased with low precision data sets (especially with 2 and 3 significant digits only).

Power testing for the jackknife and bootstrap Cis are ongoing. Jackknife could be an attractive alternative to bootstrap for small sample size.

For the smallest samples the Bayesian Deming regression can be a better option. The same is true with heteroscedastic data sets. Check package rstanbdp.

Worth mentioning that M-Deming relies on the very same recursive method proposed by Linnet the for WDeming, just with an M- algorithm for robust weight.

MM-Deming algorithm is more complex. It is also a recursive method but relies on MM- methods and uses bi-square re-descending weights. The new algorithms NgMM- and PiMM- refresh the mad() dispersion of the residuals at each iterations, making the end results much less sensible to the starting values.

Reference: http://arxiv.org/pdf/2105.04628.pdf See also: https://ssmtstatistica.wordpress.com/2024/01/27/equivariant-passing-bablok-regression-27-01-2024/

Metadata

Version

1.3.3

License

Unknown

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