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mcr (version 1.3.3)

Method Comparison Regression

Description

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, ) 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, ) and J. Raymaekers and F. Dufey (2022, ). A comprehensive overview over the implemented methods and references can be found in the manual pages "mcr-package" and "mcreg".

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Version

Install

install.packages('mcr')

Monthly Downloads

2,277

Version

1.3.3

License

GPL (>= 3)

Maintainer

Sergej Potapov

Last Published

October 11th, 2023

Functions in mcr (1.3.3)

MCResult.plot

Scatter Plot Method X vs. Method Y
MCResultAnalytical.calcResponse

Caluculate Response
MCResultBCa.calcResponse

Caluculate Response
MCResultBCa.bootstrapSummary

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

Initialize Method for 'MCResultAnalytical' Objects.
MCResultBCa.initialize

Initialize Method for 'MCResultBCa' Objects.
MCResultAnalytical.printSummary

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

Print Summary of a Regression Analysis
MCResultJackknife-class

Class "MCResultJackknife"
MCResultAnalytical-class

Class "MCResultAnalytical"
MCResultJackknife.printSummary

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

MCResult Object Initialization
MCResult.getWeights

Get Weights of Data Points
MCResultResampling-class

Class "MCResultResampling"
MCResultJackknife.initialize

Initialize Method for 'MCResultJackknife' Objects.
MCResultJackknife.getRJIF

Relative Jackknife Influence Function
MCResultJackknife.getJackknifeStatistics

Jackknife Statistics
MCResultBCa-class

Class "MCResultBCa"
MCResultBCa.printSummary

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

Plot distriblution of bootstrap pivot T
MCResultJackknife.getJackknifeIntercept

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

Plot distriblution of bootstrap coefficients
MCResultResampling.bootstrapSummary

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

Caluculate Response
MCResultJackknife.getJackknifeSlope

Get-Method for Jackknife-Slope Value.
includeLegend

Include Legend
MCResultJackknife.calcResponse

Caluculate Response
creatinine

Comparison of blood and serum creatinine measurement
mc.PBequi

Equivariant Passing-Bablok Regression
mc.analytical.ci

Analytical Confidence Interval
mc.calc.tboot

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

Quantile Method for Calculation of Resampling Confidence Intervals
MCResultJackknife.plotwithRJIF

Plotting the Relative Jackknife Influence Function
mc.calcAngleMat.R

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

Calculate Matrix of All Pair-wise Slope Angles
mc.make.CIframe

Returns Results of Calculations in Matrix Form
newMCResult

MCResult Object Constructor with Matrix in Wide Format as Input
mcreg

Comparison of Two Measurement Methods Using Regression Analysis
compareFit

Graphical Comparison of Regression Parameters and Associated Confidence Intervals
mc.wdemingConstCV

Calculate Weighted Deming Regression
newMCResultJackknife

MCResultJackknife Object Constructor with Matrix in Wide Format as Input
calcDiff

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

Jackknife Confidence Interval
mc.calcTstar

Compute Resampling T-statistic.
mc.paba.LargeData

Passing-Bablok Regression for Large Datasets
mc.paba

Passing-Bablok Regression
mc.wlinreg

Calculate Weighted Ordinary Linear Regression and Estimate Standard Errors
newMCResultAnalytical

MCResultAnalytical object constructor with matrix in wide format as input.
mcr-package

Method Comparison Regression
MCResultResampling.plotBootstrapT

Plot distriblution of bootstrap pivot T
newMCResultBCa

MCResultBCa object constructor with matrix in wide format as input.
newMCResultResampling

MCResultResampling object constructor with matrix in wide format as input.
MCResultResampling.initialize

Initialize Method for 'MCResultAnalytical' Objects.
MCResultResampling.plotBootstrapCoefficients

Plot distriblution of bootstrap coefficients
mc.calc.bca

Bias Corrected and Accelerated Resampling Confidence Interval
mc.calc.quant

Quantile Calculation for BCa
mc.linreg

Calculate ordinary linear Regression and Estimate Standard Errors
mc.deming

Calculate Unweighted Deming Regression and Estimate Standard Errors
mc.calc.Student

Student Method for Calculation of Resampling Confidence Intervals
MCResultResampling.printSummary

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

Resampling estimation of regression parameters and standard errors.
MCResult.getResiduals

Get Regression Residuals
MCResult.calcCUSUM

Calculate CUSUM Statistics According to Passing & Bablok (1983)
MCResult.calcBias

Systematical Bias Between Reference Method and Test Method
MCResult.getFitted

Get Fitted Values.
MCResult.getData

Get Data
MCResult.calcResponse

Calculate Response with Confidence Interval.
MCResult.plotResiduals

Plot Residuals of an MCResult Object
MCResult.getErrorRatio

Get Error Ratio
MCResult.getCoefficients

Get Regression Coefficients
MCResult-class

Class "MCResult"
MCResult.getRegmethod

Get Regression Method
MCResult.plotDifference

Bland-Altman Plot
MCResult.plotBias

Plot Estimated Systematical Bias with Confidence Bounds