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mcr (version 1.3.0)

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

1,200

Version

1.3.0

License

GPL (>= 3)

Maintainer

Sergej Potapov

Last Published

October 23rd, 2022

Functions in mcr (1.3.0)

MCResult.calcResponse

Calculate Response with Confidence Interval.
MCResult.getErrorRatio

Get Error Ratio
MCResult.calcBias

Systematical Bias Between Reference Method and Test Method
MCResult.printSummary

Print Summary of a Regression Analysis
MCResult.getWeights

Get Weights of Data Points
MCResultAnalytical-class

Class "MCResultAnalytical"
MCResult.getFitted

Get Fitted Values.
MCResult.getCoefficients

Get Regression Coefficients
MCResult.getData

Get Data
MCResult.initialize

MCResult Object Initialization
MCResultAnalytical.printSummary

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

Class "MCResultBCa"
MCResultBCa.plotBootstrapT

Plot distriblution of bootstrap pivot T
MCResult.plot

Scatter Plot Method X vs. Method Y
MCResultBCa.printSummary

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

Plot Estimated Systematical Bias with Confidence Bounds
MCResultJackknife.printSummary

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

Class "MCResultResampling"
MCResult.getResiduals

Get Regression Residuals
MCResultBCa.bootstrapSummary

Compute Bootstrap-Summary for 'MCResultBCa' Objects.
calcDiff

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

Graphical Comparison of Regression Parameters and Associated Confidence Intervals
MCResultBCa.calcResponse

Caluculate Response
MCResultJackknife.initialize

Initialize Method for 'MCResultJackknife' Objects.
MCResult.plotDifference

Bland-Altman Plot
MCResult.plotResiduals

Plot Residuals of an MCResult Object
MCResultJackknife.getJackknifeIntercept

Get-Method for Jackknife-Intercept Value.
MCResultJackknife.getJackknifeSlope

Get-Method for Jackknife-Slope Value.
mc.PBequi

Equivariant Passing-Bablok Regression
MCResultBCa.initialize

Initialize Method for 'MCResultBCa' Objects.
mc.analytical.ci

Analytical Confidence Interval
MCResultJackknife.plotwithRJIF

Plotting the Relative Jackknife Influence Function
mc.paba

Passing-Bablok Regression
MCResultResampling.plotBootstrapT

Plot distriblution of bootstrap pivot T
MCResultResampling.printSummary

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

Quantile Method for Calculation of Resampling Confidence Intervals
mc.wdemingConstCV

Calculate Weighted Deming Regression
mc.calcLinnetCI

Jackknife Confidence Interval
newMCResultJackknife

MCResultJackknife Object Constructor with Matrix in Wide Format as Input
newMCResultResampling

MCResultResampling object constructor with matrix in wide format as input.
MCResultAnalytical.calcResponse

Caluculate Response
mc.calc.tboot

Bootstrap-t Method for Calculation of Resampling Confidence Intervals
mcreg

Comparison of Two Measurement Methods Using Regression Analysis
mc.calc.bca

Bias Corrected and Accelerated Resampling Confidence Interval
newMCResult

MCResult Object Constructor with Matrix in Wide Format as Input
mc.calcTstar

Compute Resampling T-statistic.
MCResultAnalytical.initialize

Initialize Method for 'MCResultAnalytical' Objects.
mc.calc.quant

Quantile Calculation for BCa
mc.deming

Calculate Unweighted Deming Regression and Estimate Standard Errors
mc.make.CIframe

Returns Results of Calculations in Matrix Form
MCResultBCa.plotBootstrapCoefficients

Plot distriblution of bootstrap coefficients
mc.linreg

Calculate ordinary linear Regression and Estimate Standard Errors
MCResultJackknife-class

Class "MCResultJackknife"
MCResultResampling.bootstrapSummary

Compute Bootstrap-Summary for 'MCResultResampling' Objects.
mc.paba.LargeData

Passing-Bablok Regression for Large Datasets
MCResultResampling.calcResponse

Caluculate Response
MCResultJackknife.calcResponse

Caluculate Response
creatinine

Comparison of blood and serum creatinine measurement
MCResultJackknife.getJackknifeStatistics

Jackknife Statistics
includeLegend

Include Legend
mc.bootstrap

Resampling estimation of regression parameters and standard errors.
MCResultJackknife.getRJIF

Relative Jackknife Influence Function
mc.calc.Student

Student Method for Calculation of Resampling Confidence Intervals
mc.wlinreg

Calculate Weighted Ordinary Linear Regression and Estimate Standard Errors
MCResultResampling.initialize

Initialize Method for 'MCResultAnalytical' Objects.
mc.calcAngleMat.R

Calculate Matrix of All Pair-wise Slope Angles
MCResultResampling.plotBootstrapCoefficients

Plot distriblution of bootstrap coefficients
mcr-package

Method Comparison Regression
newMCResultAnalytical

MCResultAnalytical object constructor with matrix in wide format as input.
mc.calcAngleMat

Calculate Matrix of All Pair-wise Slope Angles
newMCResultBCa

MCResultBCa object constructor with matrix in wide format as input.
MCResult.getRegmethod

Get Regression Method
MCResult-class

Class "MCResult"
MCResult.calcCUSUM

Calculate CUSUM Statistics According to Passing & Bablok (1983)