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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/

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Version

Install

install.packages('mcrPioda')

Monthly Downloads

148

Version

1.3.4

License

GPL (>= 3)

Maintainer

Giorgio Pioda

Last Published

August 27th, 2024

Functions in mcrPioda (1.3.4)

MCResult.plotDifference

Bland-Altman Plot
MCResultBCa.plotBootstrapCoefficients

Plot distribution of bootstrap coefficients
MCResultBCa-class

Class "MCResultBCa"
MCResultBCa.bootstrapSummary

Compute Bootstrap-Summary for 'MCResultBCa' Objects.
MCResultAnalytical.printSummary

Print Regression-Analysis Summary for Objects of class 'MCResultAnalytical'.
MCResultAnalytical.initialize

Initialize Method for 'MCResultAnalytical' Objects.
MCResultBCa.plotBootstrapT

Plot distribution of bootstrap pivot T
MCResultBCa.printSummary

Print Regression-Analysis Summary for Objects of class 'MCResultBCa'.
MCResultJackknife.getRJIF

Relative Jackknife Influence Function
MCResultJackknife.getJackknifeStatistics

Jackknife Statistics
MCResultJackknife.getJackknifeIntercept

Get-Method for Jackknife-Intercept Value.
MCResultBCa.plotBoxEllipses

Plot Box Ellipses of bootstrap coefficients
MCResultJackknife.printSummary

Print Regression-Analysis Summary for Objects of class 'MCResultJackknife'.
MCResultResampling-class

Class "MCResultResampling"
MCResultBCa.calcResponse

Calculate Response
MCResultJackknife-class

Class "MCResultJackknife"
MCResultJackknife.getJackknifeSlope

Get-Method for Jackknife-Slope Value.
calcDiff

Calculate difference between two numeric vectors that gives exactly zero for very small relative differences.
MCResultResampling.calcResponse

Calculate Response
MCResultResampling.printSummary

Print Regression-Analysis Summary for Objects of class 'MCResultResampling'.
MCResultBCa.initialize

Initialize Method for 'MCResultBCa' Objects.
MCResultJackknife.calcResponse

Calculate Response
MCResultResampling.bootstrapSummary

Compute Bootstrap-Summary for 'MCResultResampling' Objects.
MCResultResampling.initialize

Initialize Method for 'MCResultAnalytical' Objects.
MCResultResampling.plotBootstrapCoefficients

Plot distribution of bootstrap coefficients
MCResultResampling.plotBoxEllipses

Plot Box Ellipses of bootstrap coefficients
MCResultResampling.plotBootstrapT

Plot distribution of bootstrap pivot T
MCResultJackknife.plotwithRJIF

Plotting the Relative Jackknife Influence Function
MCResultJackknife.initialize

Initialize Method for 'MCResultJackknife' Objects.
mc.bootstrap

Resampling estimation of regression parameters and standard errors.
mc.analytical.ci

Analytical Confidence Interval
includeLegend

Include Legend
mc.PBequi

Equivariant Passing-Bablok Regression
compareFit

Graphical Comparison of Regression Parameters and Associated Confidence Intervals
creatinine

Comparison of blood and serum creatinine measurement
mc.calc.quantile

Quantile Method for Calculation of Resampling Confidence Intervals
mc.calc.quant

Quantile Calculation for BCa
mc.calc.bca

Bias Corrected and Accelerated Resampling Confidence Interval
mc.mmNgdemingConstCV

Calculate MM Deming Regression
mc.calc.Student

Student Method for Calculation of Resampling Confidence Intervals
mc.mdemingConstCV

Calculate Weighted Deming Regression
mc.calcAngleMat.R

Calculate Matrix of All Pair-wise Slope Angles
mc.calcAngleMat

Calculate Matrix of All Pair-wise Slope Angles
mc.calcLinnetCI

Jackknife Confidence Interval
mc.calc.tboot

Bootstrap-t Method for Calculation of Resampling Confidence Intervals
mc.mmdemingConstCV

Calculate Weighted Deming Regression
mc.mmPidemingConstCV

Calculate MM Deming Regression
mc.deming

Calculate Unweighted Deming Regression and Estimate Standard Errors
mc.paba.LargeData

Passing-Bablok Regression for Large Datasets
mc.paba

Passing-Bablok Regression
mc.calcTstar

Compute Resampling T-statistic.
newMCResult

MCResult Object Constructor with Matrix in Wide Format as Input
newMCResultAnalytical

MCResultAnalytical object constructor with matrix in wide format as input.
mcreg

Comparison of Two Measurement Methods Using Regression Analysis
mcrPioda-package

Method Comparison Regression - Mcr Fork for M- and MM-Deming Regression
newMCResultResampling

MCResultResampling object constructor with matrix in wide format as input.
mc.wdemingConstCV

Calculate Weighted Deming Regression
mc.make.CIframe

Returns Results of Calculations in Matrix Form
newMCResultJackknife

MCResultJackknife Object Constructor with Matrix in Wide Format as Input
mc.linreg

Calculate ordinary linear Regression and Estimate Standard Errors
newMCResultBCa

MCResultBCa object constructor with matrix in wide format as input.
mc.wlinreg

Calculate Weighted Ordinary Linear Regression and Estimate Standard Errors
MCResult.getData

Get Data
MCResult.getFitted

Get Fitted Values.
MCResult.calcCUSUM

Calculate CUSUM Statistics According to Passing & Bablok (1983)
MCResult-class

Class "MCResult"
MCResult.getResiduals

Get Regression Residuals
MCResult.getWeights

Get Weights of Data Points
MCResultAnalytical.calcResponse

Calculate Response
MCResultAnalytical-class

Class "MCResultAnalytical"
MCResult.calcBias

Systematical Bias Between Reference Method and Test Method
MCResult.getErrorRatio

Get Error Ratio
MCResult.calcPaBaTiesRatio

Calculate PaBa Ties Ratio.
MCResult.calcResponse

Calculate Response with Confidence Interval.
MCResult.getCoefficients

Get Regression Coefficients
MCResult.initialize

MCResult Object Initialization
MCResult.printSummary

Print Summary of a Regression Analysis
MCResult.plotResiduals

Plot Residuals of an MCResult Object
MCResult.plot

Scatter Plot Method X vs. Method Y
MCResult.getRegmethod

Get Regression Method
MCResult.plotBias

Plot Estimated Systematical Bias with Confidence Bounds