| Title: | Method Comparison Regression - fork for M- and MM-Deming |
|---|---|
| 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, <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 "mcr-package" and "mcreg". |
| Authors: | Sergej Potapov [aut, cre]
 , Fabian Model [aut],
Andre Schuetzenmeister [aut]
, Ekaterina Manuilova
[aut], Florian Dufey [aut]
, Jakob Raymaekers
[aut] , Giorgio Pioda
[aut] Venkatraman E.
Seshan [ctb], Roche [cph, fnd] |
| Maintainer: | Giorgio Pioda <[email protected]> |
| License: | GPL (>= 3) |
| Version: | 1.3.3 |
| Built: | 2024-11-22 05:38:41 UTC |
| Source: | https://github.com/piodag/mcrpioda |
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 for analytical method comparison and bias estimation using patient samples.
The main function for performing regression analysis is mcreg. Various functions for summarizing
and plotting regression results are provided (see examples in mcreg).
For user site testing (installation verification) please use the test case suite provided with the package. The test case suite can be run by sourcing the 'runalltests.R' script in the 'unitTests' folder. It requires the XML and Runit packages.
| Package: | mcr |
| Type: | Package |
| Version: | 1.3.0 |
| Date: | 2022-09-13 |
| License: | GPL 3 |
| LazyLoad: | yes |
Sergej Potapov <[email protected]>, Fabian Model <[email protected]>, Andre Schuetzenmeister <[email protected]>, Ekaterina Manuilova <[email protected]>, Florian Dufey <[email protected]>, Jakob Raymaekers <[email protected]>
CLSI EP09 https://clsi.org/
Calculate difference between two numeric vectors that gives exactly zero for very small relative differences.
calcDiff(X, Y, EPS = 1e-12)calcDiff(X, Y, EPS = 1e-12)
X |
first number |
Y |
second number |
EPS |
relative difference equivalent to zero |
difference
Graphical comparison of regression parameters (intercept and slope) and their associated 100(1-alpha)% confidence intervals for multiple fitted models of 'MCResult' sub-classes.
compareFit(...)compareFit(...)
... |
list of fitted models, i.e. objects of "MCResult" sub-classes. |
No return value, instead a plot is generated
library("mcrPioda") data("creatinine", package="mcrPioda") fit.lr <- mcreg(as.matrix(creatinine), method.reg="LinReg", na.rm=TRUE) fit.wlr <- mcreg(as.matrix(creatinine), method.reg="WLinReg", na.rm=TRUE) compareFit( fit.lr, fit.wlr )library("mcrPioda") data("creatinine", package="mcrPioda") fit.lr <- mcreg(as.matrix(creatinine), method.reg="LinReg", na.rm=TRUE) fit.wlr <- mcreg(as.matrix(creatinine), method.reg="WLinReg", na.rm=TRUE) compareFit( fit.lr, fit.wlr )
This data set gives the blood and serum preoperative creatinine measurements in 110 heart surgery patients.
creatininecreatinine
A data frame containing 110 observations with serum and plasma creatinin measurements in mg/dL for each sample.
Include legend in regression plot (function plot()) or in bias plot (function plotBias ()) with two or more lines.
includeLegend( models = list(), digits = 2, design = paste(1:2), place = c("topleft", "topright", "bottomleft", "bottomright"), colors, lty = rep(1, length(models)), lwd = rep(2, length(models)), box.lty = "blank", cex = 0.8, bg = "white", inset = c(0.01, 0.01), bias = FALSE, model.names = NULL, ... )includeLegend( models = list(), digits = 2, design = paste(1:2), place = c("topleft", "topright", "bottomleft", "bottomright"), colors, lty = rep(1, length(models)), lwd = rep(2, length(models)), box.lty = "blank", cex = 0.8, bg = "white", inset = c(0.01, 0.01), bias = FALSE, model.names = NULL, ... )
models |
list of length n with Objects of class "MCResult". |
digits |
number of digits in Coefficients. |
design |
type of legend design. There are two possible designs: "1" and "2" (See example). |
place |
place for Legend: "topleft","topright","bottomleft" or "bottomright". |
colors |
vector of length n with color of regression lines. |
lty |
vector of length n with type of regression lines. |
lwd |
vector of length n with thickness of regression lines. |
box.lty |
box line-type |
cex |
numeric value representing the plotting symbol magnification factor |
bg |
the background-color of the legend box |
inset |
inset distance(s) from the margins as a fraction of the plot region when legend is placed by keyword. |
bias |
logical value. If bias = TRUE, it will be drawn a legend for |
model.names |
legend names for different models. If NULL the regression type will be used. |
... |
other parameters of function legend(). |
Legend in plot.
plot.mcr, plotBias, plotResiduals, plotDifference, compareFit
#library("mcrPioda") data(creatinine,package="mcrPioda") x <- creatinine$serum.crea y <- creatinine$plasma.crea m1 <- mcreg(x,y,method.reg="Deming", mref.name="serum.crea", mtest.name="plasma.crea", na.rm=TRUE) m2 <- mcreg(x,y,method.reg="WDeming", method.ci="jackknife", mref.name="serum.crea", mtest.name="plasma.crea", na.rm=TRUE) plot(m1, XLIM=c(0.5,3),YLIM=c(0.5,3), Legend=FALSE, Title="Deming vs. weighted Deming regression", Points.pch=19,ci.area=TRUE, ci.area.col=grey(0.9), identity=FALSE, Grid=FALSE, Sub="") plot(m2, ci.area=FALSE, ci.border=TRUE, ci.border.col="red3", reg.col="red3", Legend=FALSE,add=TRUE, Points=FALSE, identity=FALSE, Grid=FALSE) includeLegend(place="topleft",models=list(m1,m2), colors=c("darkblue","red"), design="1", digits=2)#library("mcrPioda") data(creatinine,package="mcrPioda") x <- creatinine$serum.crea y <- creatinine$plasma.crea m1 <- mcreg(x,y,method.reg="Deming", mref.name="serum.crea", mtest.name="plasma.crea", na.rm=TRUE) m2 <- mcreg(x,y,method.reg="WDeming", method.ci="jackknife", mref.name="serum.crea", mtest.name="plasma.crea", na.rm=TRUE) plot(m1, XLIM=c(0.5,3),YLIM=c(0.5,3), Legend=FALSE, Title="Deming vs. weighted Deming regression", Points.pch=19,ci.area=TRUE, ci.area.col=grey(0.9), identity=FALSE, Grid=FALSE, Sub="") plot(m2, ci.area=FALSE, ci.border=TRUE, ci.border.col="red3", reg.col="red3", Legend=FALSE,add=TRUE, Points=FALSE, identity=FALSE, Grid=FALSE) includeLegend(place="topleft",models=list(m1,m2), colors=c("darkblue","red"), design="1", digits=2)
Calculate wald confidence intervals for intercept and slope given point estimates and standard errors.
mc.analytical.ci(b0, b1, se.b0, se.b1, n, alpha)mc.analytical.ci(b0, b1, se.b0, se.b1, n, alpha)
b0 |
point estimate of intercept. |
b1 |
point estimate of slope. |
se.b0 |
standard error of intercept. |
se.b1 |
standard error of slope. |
n |
number of observations. |
alpha |
numeric value specifying the 100(1-alpha)% confidence level for the confidence interval (Default is 0.05). |
2x4 matrix of estimates and confidence intervals for intercept and slope.
Generate jackknife or (nested-) bootstrap replicates of a statistic applied to data. Only a nonparametric ballanced design is possible. For each sample calculate point estimations and standard errors for regression coefficients.
mc.bootstrap( method.reg = c("LinReg", "WLinReg", "Deming", "WDeming", "PaBa", "PaBaLarge", "TS", "PBequi","MDeming","MMDeming"), jackknife = TRUE, bootstrap = c("none", "bootstrap", "nestedbootstrap"), X, Y, error.ratio, nsamples = 1000, nnested = 25, iter.max = 30, threshold = 1e-08, NBins = 1e+06, slope.measure = c("radian", "tangent") )mc.bootstrap( method.reg = c("LinReg", "WLinReg", "Deming", "WDeming", "PaBa", "PaBaLarge", "TS", "PBequi","MDeming","MMDeming"), jackknife = TRUE, bootstrap = c("none", "bootstrap", "nestedbootstrap"), X, Y, error.ratio, nsamples = 1000, nnested = 25, iter.max = 30, threshold = 1e-08, NBins = 1e+06, slope.measure = c("radian", "tangent") )
method.reg |
Regression method. It is possible to choose between seven regression types:
|
jackknife |
Logical value. If TRUE - Jackknife based confidence interval estimation method. |
bootstrap |
Bootstrap based confidence interval estimation method. |
X |
Measurement values of reference method |
Y |
Measurement values of test method |
error.ratio |
Ratio between squared measurement errors of reference- and test method, necessary for Deming regression. Default 1. |
nsamples |
Number of bootstrap samples. |
nnested |
Number of nested bootstrap samples. |
iter.max |
maximum number of iterations for weighted Deming iterative algorithm. |
threshold |
Numerical tolerance for weighted Deming iterative algorithm convergence. |
NBins |
number of bins used when 'reg.method="PaBaLarge"' to classify each slope in one of 'NBins' bins of constant slope angle covering the range of all slopes. |
slope.measure |
angular measure of pairwise slopes used for exact PaBa regression (see |
a list consisting of
glob.coef |
Numeric vector of length two with global point estimations of intercept and slope. |
glob.sigma |
Numeric vector of length two with global estimations of standard errors of intercept and slope. |
xmean |
Global (weighted-)average of reference method values. |
B0jack |
Numeric vector with point estimations of intercept for jackknife samples. The i-th element contains point estimation for data set without i-th observation |
B1jack |
Numeric vector with point estimations of slope for jackknife samples. The i-th element contains point estimation for data set without i-th observation |
B0 |
Numeric vector with point estimations of intercept for each bootstrap sample. The i-th element contains point estimation for i-th bootstrap sample. |
B1 |
Numeric vector with point estimations of slope for each bootstrap sample. The i-th element contains point estimation for i-th bootstrap sample. |
MX |
Numeric vector with point estimations of (weighted-)average of reference method values for each bootstrap sample. The i-th element contains point estimation for i-th bootstrap sample. |
sigmaB0 |
Numeric vector with estimation of standard error of intercept for each bootstrap sample. The i-th element contains point estimation for i-th bootstrap sample. |
sigmaB1 |
Numeric vector with estimation of standard error of slope for each bootstrap sample. The i-th element contains point estimation for i-th bootstrap sample. |
nsamples |
Number of bootstrap samples. |
nnested |
Number of nested bootstrap samples. |
cimeth |
Method of confidence interval calculation (bootstrap). |
npoints |
Number of observations. |
Ekaterina Manuilova [email protected], Fabian Model [email protected], Sergej Potapov [email protected]
Efron, B., Tibshirani, R.J. (1993) An Introduction to the Bootstrap. Chapman and Hall. Carpenter, J., Bithell, J. (2000) Bootstrap confidence intervals: when, which, what? A practical guide for medical statisticians. Stat Med, 19 (9), 1141–1164.
Calculate resampling BCa confidence intervals for intercept, slope or bias given a vector of bootstrap and jackknife point estimates.
mc.calc.bca(Xboot, Xjack, xhat, alpha)mc.calc.bca(Xboot, Xjack, xhat, alpha)
Xboot |
vector of point estimates for bootstrap samples. The i-th element contains point estimate of the i-th bootstrap sample. |
Xjack |
vector of point estimates for jackknife samples. The i-th element contains point estimate of the dataset without i-th observation. |
xhat |
point estimate for the complete data set (scalar). |
alpha |
numeric value specifying the 100(1-alpha)% confidence level for the confidence interval (Default is 0.05). |
a list with elements
est |
point estimate for the complete data set (xhat). |
CI |
confidence interval for point estimate. |
Carpenter, J., Bithell, J. (2000) Bootstrap confidence intervals: when, which, what? A practical guide for medical statisticians. Stat Med, 19 (9), 1141–1164.
We are using the R default (SAS (type=3) seems bugged) quantile calculation instead of the quantile function described in Effron&Tibshirani.
mc.calc.quant(X, alpha)mc.calc.quant(X, alpha)
X |
numeric vector. |
alpha |
probabilty |
alpha-quantile of vector X.
Calculate bootstrap confidence intervals for intercept, slope or bias given the vector of bootstrap point estimates.
mc.calc.quantile(Xboot, alpha)mc.calc.quantile(Xboot, alpha)
Xboot |
vector of point estimates for bootstrap samples. The i-th element contains point estimate of the i-th bootstrap sample. |
alpha |
numeric value specifying the 100(1-alpha)% confidence level for the confidence interval (Default is 0.05). |
a list with elements
est |
median of bootstrap point estimates Xboot. |
CI |
confidence interval for point estimate 'est', calculated as quantiles. |
B. Efron and RJ. Tibshirani (1994) An Introduction to the Bootstrap. Chapman & Hall.
Calculate bootstrap confidence intervals for intercept, slope or bias given a vector of bootstrap point estimates.
mc.calc.Student(Xboot, xhat, alpha, npoints)mc.calc.Student(Xboot, xhat, alpha, npoints)
Xboot |
vector of point estimates for each bootstrap sample. The i-th element contains the point estimate of the i-th bootstrap sample. |
xhat |
global point estimate for which the confidence interval shall be computed. |
alpha |
numeric value specifying the 100(1-alpha)% confidence level for the confidence interval (Default is 0.05). |
npoints |
number of points used for the regression analysis. |
a list with elements
est |
the point estimate xhat |
se |
standard deviation computed from bootstrap point estimates Xboot |
CI |
Confidence interval for point estimate xhat, calculated as |
Carpenter, J., Bithell, J. (2000) Bootstrap confidence intervals: when, which, what? A practical guide for medical statisticians. Stat Med, 19 (9), 1141–1164.
Calculate resampling confidence intervals for intercept, slope or bias with t-Boot method given a vector of bootstrap point estimates and a vector of bootstrap standard deviations.
mc.calc.tboot(Xboot, Sboot, xhat, shat, alpha)mc.calc.tboot(Xboot, Sboot, xhat, shat, alpha)
Xboot |
vector of point estimates for bootstrap sample. The i-th element contains the point estimate for the i-th bootstrap sample. |
Sboot |
vector of standard deviations for each bootstrap sample. It schould be estimated with any analytical method or nonparametric with nested bootstrap. |
xhat |
point estimate for the complete data set (scalar). |
shat |
estimate of standard deviation for the complete data set (scalar). |
alpha |
numeric value specifying the 100(1-alpha)% confidence level for the confidence interval (Default is 0.05). |
a list with elements
est |
point estimate for the complete data set (xhat). |
se |
estimate of standard deviation for the complete data set (shat). |
CI |
confidence interval for the point estimate. |
Carpenter, J., Bithell, J. (2000) Bootstrap confidence intervals: when, which, what? A practical guide for medical statisticians. Stat Med, 19 (9), 1141–1164.
This version is implemented in C for computational efficiency.
mc.calcAngleMat(X, Y, posCor = TRUE)mc.calcAngleMat(X, Y, posCor = TRUE)
X |
measurement values of reference method. |
Y |
measurement values of test method. |
posCor |
should algorithm assume positive correlation, i.e. symmetry around slope 1? |
Upper triangular matrix of slopes for all point combinations. Slopes in radian.
This is a very slow R version. It should not be called except for debugging purposes.
mc.calcAngleMat.R(X, Y, posCor = TRUE)mc.calcAngleMat.R(X, Y, posCor = TRUE)
X |
measurement values of reference method. |
Y |
measurement values of test method. |
posCor |
should the algorithm assume positive correlation, i.e. symmetry around slope 1? |
Upper triangular matrix of slopes for all point combinations. Slopes in radian.
Calculate Jackknife confidence intervals for intercept, slope or bias given of vector of jackknife point estimates and global point estimate.
mc.calcLinnetCI(Xjack, xhat, alpha = 0.05)mc.calcLinnetCI(Xjack, xhat, alpha = 0.05)
Xjack |
vector of point estimates for jackknife samples. The i-th element contains point estimate for the dataset without the i-th observation. |
xhat |
point estimate for the complete data set (scalar). |
alpha |
numeric value specifying the 100(1-alpha)% confidence level for the confidence interval (Default is 0.05). |
a list with elements
est |
point estimate for the complete data set (scalar). |
se |
standard deviation of point estimate calculated with Jackknife Method. |
CI |
confidence interval for point estimate. |
Linnet, K. (1993) Evaluation of Regression Procedures for Methods Comparison Studies. CLIN. CHEM. 39/3, 424–432.
Compute Resampling T-statistic. for Calculation of t-Bootstrap Confidence Intervals.
mc.calcTstar(.Object, x.levels, iter.max = 30, threshold = 1e-06)mc.calcTstar(.Object, x.levels, iter.max = 30, threshold = 1e-06)
.Object |
object of class "MCResultResampling". |
x.levels |
a numeic vector of clinical desision points of interest. |
iter.max |
maximal number of iterations for calculation of weighted deming regression. |
threshold |
threshold for calculation of weighted deming regression. |
Tstar numeric vector containing resampling pivot statistic.
Carpenter J., Bithell J. Bootstrap confidence intervals: when, which, what? A practical guide for medical statisticians. Stat Med, 19 (9), 1141-1164 (2000).
Calculate Unweighted Deming Regression and Estimate Standard Errors
mc.deming(X, Y, error.ratio)mc.deming(X, Y, error.ratio)
X |
measurement values of reference method. |
Y |
measurement values of test method. |
error.ratio |
ratio of measurement error of reference method to measurement error of test method. |
a list with elements
b0 |
intercept. |
b1 |
slope. |
se.b0 |
respective standard error of intercept. |
se.b1 |
respective standard error of slope. |
xw |
average of reference method values. |
Linnet K. Evaluation of Regression Procedures for Methods Comparison Studies. CLIN. CHEM. 39/3, 424-432 (1993).
Linnet K. Estimation of the Linear Relationship between the Measurements of two Methods with Proportional Errors. STATISTICS IN MEDICINE, Vol. 9, 1463-1473 (1990).
Calculate ordinary linear Regression and Estimate Standard Errors
mc.linreg(X, Y)mc.linreg(X, Y)
X |
measurement values of reference method. |
Y |
measurement values of test method. |
a list with elements
b0 |
intercept. |
b1 |
slope. |
se.b0 |
respective standard error of intercept. |
se.b1 |
respective standard error of slope. |
xw |
average of reference method values. |
Neter J., Wassermann W., Kunter M. Applied Statistical Models. Richard D. Irwing, INC., 1985.
Returns Results of Calculations in Matrix Form
mc.make.CIframe(b0, b1, se.b0, se.b1, CI.b0, CI.b1)mc.make.CIframe(b0, b1, se.b0, se.b1, CI.b0, CI.b1)
b0 |
point estimate for intercept. |
b1 |
point estimate for slope. |
se.b0 |
standard error of intercept estimate. |
se.b1 |
standard error of slope estimate. |
CI.b0 |
numeric vector of length 2 - confidence interval for intercept. |
CI.b1 |
numeric vector of length 2 - confidence interval for slope. |
2x4 matrix of estimates and confidence intervals for intercept and slope.
Calculate M-weighted deming regression with iterative algorithm suggested by Linnet. There is no guarantee that the algorithm always converges.
mc.mdemingConstCV(X, Y, error.ratio, iter.max = 120, threshold = 1e-06)mc.mdemingConstCV(X, Y, error.ratio, iter.max = 120, threshold = 1e-06)
X |
measurement values of reference method. |
Y |
measurement values of test method. |
error.ratio |
ratio between squared measurement errors of reference- and test method, necessary for Deming regression (Default is 1). |
iter.max |
maximal number of iterations. |
threshold |
threshold value. |
a list with elements
b0 |
intercept. |
b1 |
slope. |
xw |
average of reference method values. |
iter |
number of iterations. |
Linnet K. Evaluation of Regression Procedures for Methods Comparison Studies. CLIN. CHEM. 39/3, 424-432 (1993).
Linnet K. Estimation of the Linear Relationship between the Measurements of two Methods with Proportional Errors. STATISTICS IN MEDICINE, Vol. 9, 1463-1473 (1990).
Calculate MM-weighted deming regression with iterative algorithm suggested by Linnet. There is no guarantee that the algorithm always converges.
mc.mmdemingConstCV(X, Y, error.ratio, iter.max = 120, threshold = 1e-06)mc.mmdemingConstCV(X, Y, error.ratio, iter.max = 120, threshold = 1e-06)
X |
measurement values of reference method. |
Y |
measurement values of test method. |
error.ratio |
ratio between squared measurement errors of reference- and test method, necessary for Deming regression (Default is 1). |
iter.max |
maximal number of iterations. |
threshold |
threshold value. |
a list with elements
b0 |
intercept. |
b1 |
slope. |
xw |
average of reference method values. |
iter |
number of iterations. |
Linnet K. Evaluation of Regression Procedures for Methods Comparison Studies. CLIN. CHEM. 39/3, 424-432 (1993).
Linnet K. Estimation of the Linear Relationship between the Measurements of two Methods with Proportional Errors. STATISTICS IN MEDICINE, Vol. 9, 1463-1473 (1990).
Passing-Bablok Regression
mc.paba( angM = NULL, X, Y, alpha = 0.05, posCor = TRUE, calcCI = TRUE, slope.measure = c("radian", "tangent") )mc.paba( angM = NULL, X, Y, alpha = 0.05, posCor = TRUE, calcCI = TRUE, slope.measure = c("radian", "tangent") )
angM |
upper triangular matrix of slopes for all point combinations (optional). Slopes in radian. |
X |
measurement values of reference method |
Y |
measurement values of test method |
alpha |
numeric value specifying the 100(1-alpha)% confidence level |
posCor |
should algorithm assume positive correlation, i.e. symmetry around slope 1? |
calcCI |
should confidence intervals be computed? |
slope.measure |
angular measure of pairwise slopes (see |
Matrix of estimates and confidence intervals for intercept and slope. No standard errors provided by this algorithm.
This function represents an interface to a fast C-implementation of an adaption of the Passing-Bablok algorithm for large datasets. Instead of building the complete matrix of pair-wise slope values, a pre-defined binning of slope-values is used (Default NBins=1e06). This reduces the required memory dramatically and speeds up the computation.
mc.paba.LargeData( X, Y, NBins = 1e+06, alpha = 0.05, posCor = TRUE, calcCI = TRUE, slope.measure = c("radian", "tangent") )mc.paba.LargeData( X, Y, NBins = 1e+06, alpha = 0.05, posCor = TRUE, calcCI = TRUE, slope.measure = c("radian", "tangent") )
X |
(numeric) vector containing measurement values of reference method |
Y |
(numeric) vector containing measurement values of test method |
NBins |
(integer) value specifying the number of bins used to classify slope-values |
alpha |
(numeric) value specifying the 100(1-alpha)% confidence level for confidence intervals |
posCor |
(logical) should algorithm assume positive correlation, i.e. symmetry around slope 1? |
calcCI |
(logical) should confidence intervals be computed? |
slope.measure |
angular measure of pairwise slopes (see |
Matrix of estimates and confidence intervals for intercept and slope. No standard errors provided by this algorithm.
Andre Schuetzenmeister [email protected] (partly re-using code of function 'mc.paba')
library("mcrPioda") data(creatinine,package="mcrPioda") # remove any NAs crea <- na.omit(creatinine) # call the approximative Passing-Bablok algorithm (Default NBins=1e06) res1 <- mcreg(x=crea[,1], y=crea[,2], method.reg="PaBaLarge", method.ci="analytical") getCoefficients(res1) # now increase the number of bins and see whether this makes a difference res2 <- mcreg(x=crea[,1], y=crea[,2], method.reg="PaBaLarge", method.ci="analytical", NBins=1e07) getCoefficients(res2) getCoefficients(res1)-getCoefficients(res2)library("mcrPioda") data(creatinine,package="mcrPioda") # remove any NAs crea <- na.omit(creatinine) # call the approximative Passing-Bablok algorithm (Default NBins=1e06) res1 <- mcreg(x=crea[,1], y=crea[,2], method.reg="PaBaLarge", method.ci="analytical") getCoefficients(res1) # now increase the number of bins and see whether this makes a difference res2 <- mcreg(x=crea[,1], y=crea[,2], method.reg="PaBaLarge", method.ci="analytical", NBins=1e07) getCoefficients(res2) getCoefficients(res1)-getCoefficients(res2)
This is an implementation of the equivariant Passing-Bablok regression.
mc.PBequi( X, Y, alpha = 0.05, slope.measure = c("radian", "tangent"), method.reg = c("PBequi", "TS"), extended.output = FALSE, calcCI = TRUE, methodlarge = TRUE )mc.PBequi( X, Y, alpha = 0.05, slope.measure = c("radian", "tangent"), method.reg = c("PBequi", "TS"), extended.output = FALSE, calcCI = TRUE, methodlarge = TRUE )
X |
measurement values of reference method |
Y |
measurement values of test method |
alpha |
numeric value specifying the 100(1-alpha)% confidence level |
slope.measure |
angular measure of pairwise slopes (see |
method.reg |
"PBequi" equivariant Passing-Bablok regression; "TS" Theil-Sen regression |
extended.output |
boolean. If TRUE, several intermediate results are returned |
calcCI |
boolean. If TRUE, sd of intercept and slope as well as xw are calculated |
methodlarge |
If TRUE (default), quasilinear method is used, if FALSE, quadratic method is used |
a list with elements.
b0 |
intercept. |
b1 |
slope. |
se.b0 |
respective standard error of intercept. |
se.b1 |
respective standard error of slope. |
xw |
weighted average of reference method values. |
weight |
dummy values, only returned it extended.output=FALSE. |
sez |
variance of intercept for fixed slope (extended.output=TRUE, only). |
vartau |
variance of Kendall's tau (extended.output=TRUE, only). |
covtx |
covariance of tau and zeta (extended.output=TRUE, only). |
x0 |
"center of gravity" of x (extended.output=TRUE, only). |
taui |
"Inversion vector; Indicator of influence" |
Calculate weighted deming regression with iterative algorithm suggested by Linnet. This algorithm is avalaible only for positive values. But even in this case there is no guarantee that the algorithm always converges.
mc.wdemingConstCV(X, Y, error.ratio, iter.max = 30, threshold = 1e-06)mc.wdemingConstCV(X, Y, error.ratio, iter.max = 30, threshold = 1e-06)
X |
measurement values of reference method. |
Y |
measurement values of test method. |
error.ratio |
ratio between squared measurement errors of reference- and test method, necessary for Deming regression (Default is 1). |
iter.max |
maximal number of iterations. |
threshold |
threshold value. |
a list with elements
b0 |
intercept. |
b1 |
slope. |
xw |
average of reference method values. |
iter |
number of iterations. |
Linnet K. Evaluation of Regression Procedures for Methods Comparison Studies. CLIN. CHEM. 39/3, 424-432 (1993).
Linnet K. Estimation of the Linear Relationship between the Measurements of two Methods with Proportional Errors. STATISTICS IN MEDICINE, Vol. 9, 1463-1473 (1990).
The weights of regression are taken as reverse squared values of the reference method, that's why it is impossible to achieve the calculations for zero values.
mc.wlinreg(X, Y)mc.wlinreg(X, Y)
X |
measurement values of reference method. |
Y |
measurement values of test method. |
a list with elements.
b0 |
intercept. |
b1 |
slope. |
se.b0 |
respective standard error of intercept. |
se.b1 |
respective standard error of slope. |
xw |
weighted average of reference method values. |
Neter J., Wassermann W., Kunter M. Applied Statistical Models. Richard D. Irwing, INC., 1985.
mcreg is used to compare two measurement methods by means of regression analysis.
Available methods comprise ordinary and weighted linear regression,
Deming and weighted Deming regression and Passing-Bablok regression. Point estimates of regression
parameters are computed together with their standard errors and confidence intervals.
mcreg( x, y = NULL, error.ratio = 1, alpha = 0.05, mref.name = NULL, mtest.name = NULL, sample.names = NULL, method.reg = c("PaBa", "LinReg", "WLinReg", "Deming", "WDeming", "PaBaLarge", "PBequi", "TS","MDeming","MMDeming"), method.ci = c("bootstrap", "jackknife", "analytical", "nestedbootstrap"), method.bootstrap.ci = c("quantile", "Student", "BCa", "tBoot"), nsamples = 999, nnested = 25, rng.seed = NULL, rng.kind = "Mersenne-Twister", iter.max = 30, threshold = 1e-06, na.rm = FALSE, NBins = 1e+06, slope.measure = c("radian", "tangent"), methodlarge = TRUE )mcreg( x, y = NULL, error.ratio = 1, alpha = 0.05, mref.name = NULL, mtest.name = NULL, sample.names = NULL, method.reg = c("PaBa", "LinReg", "WLinReg", "Deming", "WDeming", "PaBaLarge", "PBequi", "TS","MDeming","MMDeming"), method.ci = c("bootstrap", "jackknife", "analytical", "nestedbootstrap"), method.bootstrap.ci = c("quantile", "Student", "BCa", "tBoot"), nsamples = 999, nnested = 25, rng.seed = NULL, rng.kind = "Mersenne-Twister", iter.max = 30, threshold = 1e-06, na.rm = FALSE, NBins = 1e+06, slope.measure = c("radian", "tangent"), methodlarge = TRUE )
x |
measurement values of reference method, or two column matrix. |
y |
measurement values of test method. |
error.ratio |
ratio between squared measurement errors of reference and test method, necessary for Deming regression (Default 1). |
alpha |
value specifying the 100(1-alpha)% confidence level for confidence intervals (Default is 0.05). |
mref.name |
name of reference method (Default "Method1"). |
mtest.name |
name of test Method (Default "Method2"). |
sample.names |
names of cases (Default "S##"). |
method.reg |
regression method. It is possible to choose between five regression methods:
|
method.ci |
method of confidence interval calculation. The function
contains four basic methods for calculation of confidence intervals for regression coefficients.
|
method.bootstrap.ci |
bootstrap based confidence interval estimation method. |
nsamples |
number of bootstrap samples. |
nnested |
number of nested bootstrap samples. |
rng.seed |
integer number that sets the random number generator seed for bootstrap sampling. If set to NULL currently in the R session used RNG setting will be used. |
rng.kind |
type of random number generator for bootstrap sampling. Only used when rng.seed is specified, see set.seed for details. |
iter.max |
maximum number of iterations for weighted Deming iterative algorithm. |
threshold |
numerical tolerance for weighted Deming iterative algorithm convergence. |
na.rm |
remove measurement pairs that contain missing values (Default is FALSE). |
NBins |
number of bins used when 'reg.method="PaBaLarge"' to classify each slope in one of 'NBins' bins covering the range of all slopes |
slope.measure |
angular measure of pairwise slopes used for exact PaBa regression (see below for details). |
methodlarge |
Boolean. This parameter applies only to regmethod="PBequi" and "TS". If TRUE, a quasilinear algorithm is used. If FALSE, a quadratic algorithm is used which is faster for less than several hundred data pairs. |
The regression analysis yields regression coefficients 'Inercept' and 'Slope' of the regression
. There are methods for computing the systematical
bias between reference and test method at a decision point Xc, ,
accompanied by its corresponding standard error and confidence interval. One can use plotting
method plotBias for a comprehensive view of the systematical bias.
Weighted regression for heteroscedastic data is available for linear and Deming regression and implemented as a data point weighting with the inverted squared value of the reference method. Therefore calculation of weighted regression (linear and Deming) is available only for positive values (>0). Passing-Bablok regression is only available for non-negative values (>=0).
Confidence intervals for regression parameters and bias estimates are calculated either by using analytical methods or by means of resampling methods ("jackknife", "bootstrap", "nested bootstrap"). An analytical method is available for all types of regression except for weighted Deming. For Passing-Bablok regression the option "analytical" calculates confidence intervals for the regression parameters according to the non-parametric approach given in the original reference.
The "jackknife" (or leave one out resampling) method was suggested by Linnet for calculating confidence intervals of regression parameters of Deming and weighted Deming regression. It is possible to calculate jackknife confidence intervals for all types of regression. Note that we do not recommend this method for Passing-Bablok since it has a tendency of underestimating the variability (jackknife is known to yield incorrect estimates for errors of quantiles).
The bootstrap method requires additionally choosing a value for method.bootstrap.ci.
If bootstrap is the method of choice, "BCa", t-bootstrap ("tBoot") and simple "quantile" confidence intervals are recommended
(See Efron B. and Tibshirani R.J.(1993),Carpenter J., Bithell J. (2000)).
The "nestedbootstrap" method can be very time-consuming but is necessary for calculating t-bootstrap
confidence intervals for weighted Deming or Passing-Bablok regression. For these regression methods there are no analytical
solutions for computing standard errors, which therefore have to be obtained by nested bootstrapping.
Note that estimating resampling based confidence intervals for Passing–Bablok regressions can take very long time
for larger data sets due to the high computational complexity of the algorithm. To mitigate this drawback
an adaption of the Passing-Bablok algorithm has been implemented ("PaBaLarge"), which yields approximative results. This approach
does not build the complete upper triangular matrix of all 'n*(n-1)/2' slopes. It subdivides the range of slopes into
'NBins' classes, and sorts each slope into one of these bins. The remaining steps are the same as for the exact "PaBa"
algorithm, except that these are performed on the binned slopes instead of operating on the matrix of slopes.
Our implementation of exact Passing-Bablok regression ("PaBa") provides two alternative metrics for regression slopes which
can result in different regression estimates.
As a robust regression method PaBa is essentially invariant to the parameterization of regression slopes,
however in the case of an even number of all pairwise slopes the two central slopes are averaged to estimate the final regression slope.
In this situation using an angle based metric (slope.measure="radian") will result in
a regression estimate that is geometrically centered between the two central slopes, whereas the tangent measure (slope.measure="tangent")
proposed in Passing and Bablok (1983) will be geometrically biased towards a higher slope. See below for a pathological example.
Note that the difference between the two measures is negligible for data sets with reasonable sample size (N>20) and correlation.
Equivariant Passing-Bablok regression as proposed by Bablok et al. (1988) (see also Dufey 2020) is not bound to slopes near 1 and therefore not only applicable for method comparison but also for method transformation, i.e., when two methods yield results on a different scale. Like ordinary Passing-Bablok regression, the method is robust. This method should be preferred over the older "PaBa" and "PaBalarge" algorithms. Both slope measures "radian" and "tangent" are available as are methods for the determination of confidence intervals -analytical and bootstrap. By default (methodlarge=TRUE), a modified algorithm (Dillencourt et al., 1992) is used which scales quasilinearly and requires little memory. Alternatively (methodlarge=F), a simpler implementation which scales quadratically and is more memory intensive may be called. While point estimates coincide for both implementations, analytic confidence intervals differ slightly. Same holds true for the Theil-Sen estimator, which is a robust alternative to linear regression. Like linear regression, it assumes that x-values are error free.
"MCResult" object containing regression results. The function getCoefficients or
printSummary can be used to obtain or print a summary of the results.
The function getData allows to see the original data.
An S4 object of class "MCResult" containing at least the following slots:
data |
measurement data in wide format, one pair of observations per sample. Includes samples ID, reference measurement, test measurement. |
para |
numeric matrix with estimates for slope and intercept, corresponding standard deviations and confidence intervals. |
mnames |
character vector of length two containing names of analytical methods. |
regmeth |
type of regression type used for parameter estimation. |
cimeth |
method used for calculation of confidence intervals. |
error.ratio |
ratio between squared measurement errors of reference and test method, necessary for Deming regression. |
alpha |
confidence level using for calculation of confidence intervals. |
Ekaterina Manuilova [email protected], Andre Schuetzenmeister [email protected], Fabian Model [email protected], Sergej Potapov [email protected], Florian Dufey [email protected], Jakob Raymaekers [email protected]
Bland, J. M., Altman, D. G. (1986) Statistical methods for assessing agreement between two methods of clinical measurement. Lancet, i: 307–310.
Linnet, K. (1993) Evaluation of Regression Procedures for Methods Comparison Studies. CLIN. CHEM. 39/3, 424–432.
Linnet, K. (1990) Estimation of the Linear Relationship between the Measurements of two Methods with Proportional Errors. Statistics in Medicine, Vol. 9, 1463–1473.
Neter, J., Wassermann, W., Kunter, M. (1985) Applied Statistical Models. Richard D. Irwing, INC.
Looney, S. W. (2010) Statistical Methods for Assessing Biomarkers. Methods in Molecular Biology, vol. 184: Biostatistical Methods. Human Press INC.
Passing, H., Bablok, W. (1983) A new biometrical procedure for testing the equality of measurements from two different analytical methods. Application of linear regression procedures for method comparison studies in clinical chemistry, Part I. J Clin Chem Clin Biochem. Nov; 21(11):709–20.
Bablok, W., Passing, H., Bender, R., & Schneider, B. (1988) A general regression procedure for method transformation. Application of linear regression procedures for method comparison studies in clinical chemistry, Part III. Clinical Chemistry and Laboratory Medicine, 26(11): 783–790.
Dillencourt, M. B., Mount, D. M., & Netanyahu, N. S. (1992) A randomized algorithm for slope selection. International Journal of Computational Geometry & Applications, 2(01): 1–27.
Dufey, F. (2020) Derivation of Passing-Bablok regression from Kendall's tau. The International Journal of Biostatistics, 16(2): 20190157. https://doi.org/10.1515/ijb-2019-0157
Raymaekers, J., Dufey, F. (2022) Equivariant Passing-Bablok regression in quasilinear time. arXiv preprint arXiv:2202.08060. https://doi.org/10.48550/arXiv.2202.08060
Efron, B., Tibshirani, R.J. (1993) An Introduction to the Bootstrap. Chapman and Hall.
Carpenter, J., Bithell, J. (2000) Bootstrap confidence intervals: when, which, what? A practical guide for medical statisticians. Stat Med, 19 (9), 1141–1164.
CLSI EP9-A2. Method Comparison and Bias Estimation Using Patient Samples; Approved Guideline.
plotDifference, plot.mcr, getResiduals, plotResiduals, calcResponse, calcBias, plotBias, compareFit
library("mcrPioda") data(creatinine,package="mcrPioda") x <- creatinine$serum.crea y <- creatinine$plasma.crea # Deming regression fit. # The confidence intercals for regression coefficients # are calculated with analytical method model1<- mcreg(x,y,error.ratio=1,method.reg="Deming", method.ci="analytical", mref.name = "serum.crea", mtest.name = "plasma.crea", na.rm=TRUE) # Results printSummary(model1) getCoefficients(model1) plot(model1) # Deming regression fit. # The confidence intervals for regression coefficients # are calculated with bootstrap (BCa) method model2<- mcreg(x,y,error.ratio=1,method.reg="Deming", method.ci="bootstrap", method.bootstrap.ci = "BCa", mref.name = "serum.crea", mtest.name = "plasma.crea", na.rm=TRUE) compareFit(model1, model2) ## Pathological example of Passing-Bablok regression where measure for slope angle matters x1 <- 1:10; y1 <- 0.5*x1; x <- c(x1,y1); y <- c(y1,x1) m1 <- mcreg(x,y,method.reg="PaBa",method.ci="analytical",slope.measure="radian", mref.name="X",mtest.name="Y") m2 <- mcreg(x,y,method.reg="PaBa",method.ci="analytical",slope.measure="tangent", mref.name="X",mtest.name="Y") plot(m1, add.legend=FALSE,identity=FALSE, main="Radian vs. tangent slope measures in Passing-Bablok regression\n(pathological example)", ci.area=FALSE,add.cor=FALSE) plot(m2, ci.area=FALSE,reg.col="darkgreen",reg.lty=2,identity=FALSE,add.legend=FALSE, draw.points=FALSE,add=TRUE,add.cor=FALSE) includeLegend(place="topleft",models=list(m1,m2),model.names=c("PaBa Radian","PaBa Tangent"), colors=c("darkblue","darkgreen"),lty=c(1,2),design="1",digits=2)library("mcrPioda") data(creatinine,package="mcrPioda") x <- creatinine$serum.crea y <- creatinine$plasma.crea # Deming regression fit. # The confidence intercals for regression coefficients # are calculated with analytical method model1<- mcreg(x,y,error.ratio=1,method.reg="Deming", method.ci="analytical", mref.name = "serum.crea", mtest.name = "plasma.crea", na.rm=TRUE) # Results printSummary(model1) getCoefficients(model1) plot(model1) # Deming regression fit. # The confidence intervals for regression coefficients # are calculated with bootstrap (BCa) method model2<- mcreg(x,y,error.ratio=1,method.reg="Deming", method.ci="bootstrap", method.bootstrap.ci = "BCa", mref.name = "serum.crea", mtest.name = "plasma.crea", na.rm=TRUE) compareFit(model1, model2) ## Pathological example of Passing-Bablok regression where measure for slope angle matters x1 <- 1:10; y1 <- 0.5*x1; x <- c(x1,y1); y <- c(y1,x1) m1 <- mcreg(x,y,method.reg="PaBa",method.ci="analytical",slope.measure="radian", mref.name="X",mtest.name="Y") m2 <- mcreg(x,y,method.reg="PaBa",method.ci="analytical",slope.measure="tangent", mref.name="X",mtest.name="Y") plot(m1, add.legend=FALSE,identity=FALSE, main="Radian vs. tangent slope measures in Passing-Bablok regression\n(pathological example)", ci.area=FALSE,add.cor=FALSE) plot(m2, ci.area=FALSE,reg.col="darkgreen",reg.lty=2,identity=FALSE,add.legend=FALSE, draw.points=FALSE,add=TRUE,add.cor=FALSE) includeLegend(place="topleft",models=list(m1,m2),model.names=c("PaBa Radian","PaBa Tangent"), colors=c("darkblue","darkgreen"),lty=c(1,2),design="1",digits=2)
"MCResult"
Result of a method comparison.
Object is typically created by a call to function mcreg.
Object can be directly constructed by calling newMCResult or new("MCResult", data, para, mnames, regmeth, cimeth, error.ratio, alpha, weight).
data:Object of class "data.frame" ~~
para:Object of class "matrix" ~~
mnames:Object of class "character" ~~
regmeth:Object of class "character" ~~
cimeth:Object of class "character" ~~
error.ratio:Object of class "numeric" ~~
alpha:Object of class "numeric" ~~
weight:Object of class "numeric" ~~
signature(.Object = "MCResult"): ...
signature(.Object = "MCResult"): ...
signature(.Object = "MCResult"): ...
signature(.Object = "MCResult"): ...
signature(.Object = "MCResult"): ...
signature(.Object = "MCResult"): ...
signature(.Object = "MCResult"): ...
signature(.Object = "MCResult"): ...
signature(.Object = "MCResult"): ...
signature(.Object = "MCResult"): ...
signature(x = "MCResult"): ...
signature(x = "MCResult"): ...
signature(.Object = "MCResult"): ...
signature(.Object = "MCResult"): ...
signature(.Object = "MCResult"): ...
signature(.Object = "MCResult"): ...
Ekaterina Manuilova [email protected], Andre Schuetzenmeister [email protected], Fabian Model [email protected] Sergej Potapov [email protected]
showClass("MCResult")showClass("MCResult")
Calculate systematical bias between reference and test methods
at the decision point Xc as
with corresponding confidence intervals.
MCResult.calcBias( .Object, x.levels, type = c("absolute", "proportional"), percent = TRUE, alpha = 0.05, ... )MCResult.calcBias( .Object, x.levels, type = c("absolute", "proportional"), percent = TRUE, alpha = 0.05, ... )
.Object |
object of class "MCResult". |
x.levels |
a numeric vector with decision points for which bias schould be calculated. |
type |
One can choose between absolute (default) and proportional bias ( |
percent |
logical value. If |
alpha |
numeric value specifying the 100(1- |
... |
further parameters |
response and corresponding confidence interval for each decision point from x.levels.
#library("mcrPioda") data(creatinine,package="mcrPioda") x <- creatinine$serum.crea y <- creatinine$plasma.crea # Deming regression fit. # The confidence intervals for regression coefficients # are calculated with analytical method model <- mcreg( x,y,error.ratio = 1,method.reg = "Deming", method.ci = "analytical", mref.name = "serum.crea", mtest.name = "plasma.crea", na.rm=TRUE ) # Now we calculate the systematical bias # between the testmethod and the reference method # at the medical decision points 1, 2 and 3 calcBias( model, x.levels = c(1,2,3)) calcBias( model, x.levels = c(1,2,3), type = "proportional") calcBias( model, x.levels = c(1,2,3), type = "proportional", percent = FALSE)#library("mcrPioda") data(creatinine,package="mcrPioda") x <- creatinine$serum.crea y <- creatinine$plasma.crea # Deming regression fit. # The confidence intervals for regression coefficients # are calculated with analytical method model <- mcreg( x,y,error.ratio = 1,method.reg = "Deming", method.ci = "analytical", mref.name = "serum.crea", mtest.name = "plasma.crea", na.rm=TRUE ) # Now we calculate the systematical bias # between the testmethod and the reference method # at the medical decision points 1, 2 and 3 calcBias( model, x.levels = c(1,2,3)) calcBias( model, x.levels = c(1,2,3), type = "proportional") calcBias( model, x.levels = c(1,2,3), type = "proportional", percent = FALSE)
Calculate CUSUM Statistics According to Passing & Bablok (1983)
MCResult.calcCUSUM(.Object)MCResult.calcCUSUM(.Object)
.Object |
object of class "MCResult". |
A list containing the following elements:
nPos |
sum of positive residuals |
nNeg |
sum of negative residuals |
cusum |
a cumulative sum of vector with scores ri for each point, sorted increasing by distance of points to regression line. |
max.cumsum |
Test statisics of linearity test |
Passing, H., Bablok, W. (1983) A new biometrical procedure for testing the equality of measurements from two different analytical methods. Application of linear regression procedures for method comparison studies in clinical chemistry, Part I. J Clin Chem Clin Biochem. Nov; 21(11):709–20.
Calculate Response with Corresponding Confidence Interval
MCResult.calcResponse(.Object, x.levels, alpha, ...)MCResult.calcResponse(.Object, x.levels, alpha, ...)
.Object |
object of class "MCResult". |
x.levels |
a numeric vector with points for which response schould be calculated. |
alpha |
numeric value specifying the 100(1- |
... |
further parameters |
response and corresponding confidence interval for each point in vector x.levels.
#library("mcrPioda") data(creatinine,package="mcrPioda") x <- creatinine$serum.crea y <- creatinine$plasma.crea # Deming regression fit. # The confidence intercals for regression coefficients # are calculated with analytical method model <- mcreg( x,y,error.ratio=1,method.reg="Deming", method.ci="analytical", mref.name = "serum.crea", mtest.name = "plasma.crea", na.rm=TRUE ) calcResponse(model, x.levels=c(1,2,3))#library("mcrPioda") data(creatinine,package="mcrPioda") x <- creatinine$serum.crea y <- creatinine$plasma.crea # Deming regression fit. # The confidence intercals for regression coefficients # are calculated with analytical method model <- mcreg( x,y,error.ratio=1,method.reg="Deming", method.ci="analytical", mref.name = "serum.crea", mtest.name = "plasma.crea", na.rm=TRUE ) calcResponse(model, x.levels=c(1,2,3))
Get Regression Coefficients
MCResult.getCoefficients(.Object)MCResult.getCoefficients(.Object)
.Object |
object of class "MCResult". |
Regression parameters in matrix form. Rows: Intercept, Slope. Cols: EST, SE, LCI, UCI.
Get Data
MCResult.getData(.Object)MCResult.getData(.Object)
.Object |
object of class "MCResult". |
Measurement data in matrix format. First column contains reference method (X), second column contains test method (Y).
Get Error Ratio
MCResult.getErrorRatio(.Object)MCResult.getErrorRatio(.Object)
.Object |
Object of class "MCResult" |
Error ratio. Only relevant for Deming type regressions.
This funcion computes fitted values for a 'MCResult'-object. Depending on the regression method and the error ratio, a projection onto the regression line is performed accordingly. For each point (x_i; y_i) i=1,...,n the projected point(x_hat_i; y_hat_i) is computed.
MCResult.getFitted(.Object)MCResult.getFitted(.Object)
.Object |
object of class "MCResult". |
fitted values as data frame.
Get Regression Method
MCResult.getRegmethod(.Object)MCResult.getRegmethod(.Object)
.Object |
object of class "MCResult". |
Name of the statistical method used for the regression analysis.
This function returns residuals in x-direction (x-xhat), in y-direction(y-yhat) and optimized residuals. The optimized residuals correspond to distances between data points and the regression line which were optimized for regression coefficients estimation. In case of Passing-Bablok Regression orthogonal residuals will be returned as optimized residuals . The residuals in x-direction are interesting for regression types which assume errors in both variables (deming, weighted deming, Passing-Bablok), particularily for checking of model assumptions.
MCResult.getResiduals(.Object)MCResult.getResiduals(.Object)
.Object |
object of class "MCResult". |
residuals as data frame.
Get Weights of Data Points
MCResult.getWeights(.Object)MCResult.getWeights(.Object)
.Object |
Object of class "MCResult" |
Weights of data points.
MCResult Object Initialization
MCResult.initialize( .Object, data = data.frame(X = NA, Y = NA), para = matrix(NA, ncol = 4, nrow = 2), mnames = c("unknown", "unknown"), regmeth = "unknown", cimeth = "unknown", error.ratio = 0, alpha = 0.05, weight = 1 )MCResult.initialize( .Object, data = data.frame(X = NA, Y = NA), para = matrix(NA, ncol = 4, nrow = 2), mnames = c("unknown", "unknown"), regmeth = "unknown", cimeth = "unknown", error.ratio = 0, alpha = 0.05, weight = 1 )
.Object |
object of class "MCResult" |
data |
measurement data in matrix format. First column reference method (x), second column test method (y). |
para |
regression parameters in matrix form. Rows: Intercept, Slope. Cols: EST, SE, LCI, UCI. |
mnames |
names of reference and test method. |
regmeth |
name of statistical method used for regression. |
cimeth |
name of statistical method used for computing confidence intervals. |
error.ratio |
ratio between standard deviation of reference and test method. |
alpha |
numeric value specifying the 100(1- |
weight |
weights to be used for observations |
MCResult object with initialized parameter.
Plot method X (reference) vs. method Y (test) with (optional) line of identity, regression line and confidence bounds for response.
MCResult.plot( x, alpha = 0.05, xn = 20, equal.axis = FALSE, xlim = NULL, ylim = NULL, xaxp = NULL, yaxp = NULL, x.lab = x@mnames[1], y.lab = x@mnames[2], add = FALSE, draw.points = TRUE, points.col = "black", points.pch = 1, points.cex = 0.8, reg = TRUE, reg.col = NULL, reg.lty = 1, reg.lwd = 2, identity = TRUE, identity.col = NULL, identity.lty = 2, identity.lwd = 1, ci.area = TRUE, ci.area.col = NULL, ci.border = FALSE, ci.border.col = NULL, ci.border.lty = 2, ci.border.lwd = 1, add.legend = TRUE, legend.place = c("topleft", "topright", "bottomleft", "bottomright"), main = NULL, sub = NULL, add.cor = TRUE, cor.method = c("pearson", "kendall", "spearman"), add.grid = TRUE, digits = list(coef = 2, cor = 3), ... )MCResult.plot( x, alpha = 0.05, xn = 20, equal.axis = FALSE, xlim = NULL, ylim = NULL, xaxp = NULL, yaxp = NULL, x.lab = x@mnames[1], y.lab = x@mnames[2], add = FALSE, draw.points = TRUE, points.col = "black", points.pch = 1, points.cex = 0.8, reg = TRUE, reg.col = NULL, reg.lty = 1, reg.lwd = 2, identity = TRUE, identity.col = NULL, identity.lty = 2, identity.lwd = 1, ci.area = TRUE, ci.area.col = NULL, ci.border = FALSE, ci.border.col = NULL, ci.border.lty = 2, ci.border.lwd = 1, add.legend = TRUE, legend.place = c("topleft", "topright", "bottomleft", "bottomright"), main = NULL, sub = NULL, add.cor = TRUE, cor.method = c("pearson", "kendall", "spearman"), add.grid = TRUE, digits = list(coef = 2, cor = 3), ... )
x |
object of class "MCResult". |
alpha |
numeric value specifying the 100(1- |
xn |
number of points (default 20) for calculation of confidence bounds. |
equal.axis |
logical value. If |
xlim |
limits of the x-axis. If |
ylim |
limits of the y-axis. If |
xaxp |
ticks of the x-axis. If |
yaxp |
ticks of the y-axis. If |
x.lab |
label of x-axis. Default is the name of reference method. |
y.lab |
label of y-axis. Default is the name of test method. |
add |
logical value. If |
draw.points |
logical value. If |
points.col |
Color of data points. |
points.pch |
Type of data points (see |
points.cex |
Size of data points (see |
reg |
Logical value. If |
reg.col |
Color of regression line. |
reg.lty |
Type of regression line. |
reg.lwd |
The width of regression line. |
identity |
logical value. If |
identity.col |
The color of identity line. |
identity.lty |
The type of identity line. |
identity.lwd |
the width of identity line. |
ci.area |
logical value. If |
ci.area.col |
the color of confidence area. |
ci.border |
logical value. If |
ci.border.col |
The color of confidence limits. |
ci.border.lty |
The line type of confidence limits. |
ci.border.lwd |
The line width of confidence limits. |
add.legend |
logical value. If |
legend.place |
The position of legend: "topleft","topright","bottomleft","bottomright". |
main |
String value. The main title of plot. If |
sub |
String value. The subtitle of plot. If |
add.cor |
Logical value. If |
cor.method |
a character string indicating which correlation coefficient is to be computed. One of "pearson" (default), "kendall", or "spearman", can be abbreviated. |
add.grid |
Logical value. If |
digits |
list with the number of digits for the regression equation and the correlation coefficient. |
... |
further graphical parameters |
No return value, instead a plot is generated
plotBias, plotResiduals, plotDifference, compareFit,includeLegend
library(mcrPioda) data(creatinine,package="mcrPioda") creatinine <- creatinine[complete.cases(creatinine),] x <- creatinine$serum.crea y <- creatinine$plasma.crea m1 <- mcreg(x,y,method.reg="Deming", mref.name="serum.crea", mtest.name="plasma.crea", na.rm=TRUE) m2 <- mcreg(x,y,method.reg="WDeming", method.ci="jackknife", mref.name="serum.crea", mtest.name="plasma.crea", na.rm=TRUE) plot(m1, xlim=c(0.5,3),ylim=c(0.5,3), add.legend=FALSE, main="Deming vs. weighted Deming regression", points.pch=19,ci.area=TRUE, ci.area.col=grey(0.9), identity=FALSE, add.grid=FALSE, sub="") plot(m2, ci.area=FALSE, ci.border=TRUE, ci.border.col="red3", reg.col="red3", add.legend=FALSE, draw.points=FALSE,add=TRUE) includeLegend(place="topleft",models=list(m1,m2), colors=c("darkblue","red"), design="1", digits=2)library(mcrPioda) data(creatinine,package="mcrPioda") creatinine <- creatinine[complete.cases(creatinine),] x <- creatinine$serum.crea y <- creatinine$plasma.crea m1 <- mcreg(x,y,method.reg="Deming", mref.name="serum.crea", mtest.name="plasma.crea", na.rm=TRUE) m2 <- mcreg(x,y,method.reg="WDeming", method.ci="jackknife", mref.name="serum.crea", mtest.name="plasma.crea", na.rm=TRUE) plot(m1, xlim=c(0.5,3),ylim=c(0.5,3), add.legend=FALSE, main="Deming vs. weighted Deming regression", points.pch=19,ci.area=TRUE, ci.area.col=grey(0.9), identity=FALSE, add.grid=FALSE, sub="") plot(m2, ci.area=FALSE, ci.border=TRUE, ci.border.col="red3", reg.col="red3", add.legend=FALSE, draw.points=FALSE,add=TRUE) includeLegend(place="topleft",models=list(m1,m2), colors=c("darkblue","red"), design="1", digits=2)
This function plots the estimated systematical bias
with confidence bounds, covering the whole range of reference method X
or only part of it.
MCResult.plotBias( x, xn = 100, alpha = 0.05, add = FALSE, prop = FALSE, xlim = NULL, ylim = NULL, bias = TRUE, bias.lty = 1, bias.lwd = 2, bias.col = NULL, ci.area = TRUE, ci.area.col = NULL, ci.border = FALSE, ci.border.col = NULL, ci.border.lwd = 1, ci.border.lty = 2, zeroline = TRUE, zeroline.col = NULL, zeroline.lty = 2, zeroline.lwd = 1, main = NULL, sub = NULL, add.grid = TRUE, xlab = NULL, ylab = NULL, cut.point = NULL, cut.point.col = "red", cut.point.lwd = 2, cut.point.lty = 1, ... )MCResult.plotBias( x, xn = 100, alpha = 0.05, add = FALSE, prop = FALSE, xlim = NULL, ylim = NULL, bias = TRUE, bias.lty = 1, bias.lwd = 2, bias.col = NULL, ci.area = TRUE, ci.area.col = NULL, ci.border = FALSE, ci.border.col = NULL, ci.border.lwd = 1, ci.border.lty = 2, zeroline = TRUE, zeroline.col = NULL, zeroline.lty = 2, zeroline.lwd = 1, main = NULL, sub = NULL, add.grid = TRUE, xlab = NULL, ylab = NULL, cut.point = NULL, cut.point.col = "red", cut.point.lwd = 2, cut.point.lty = 1, ... )
x |
object of class "MCResult". |
xn |
# number of poits for drawing of confidence bounds/area. |
alpha |
numeric value specifying the 100(1- |
add |
logical value. If |
prop |
a logical value. If |
xlim |
limits of the x-axis. If |
ylim |
limits of the y-axis. If |
bias |
logical value. If |
bias.lty |
type of the bias line. |
bias.lwd |
width of the bias line. |
bias.col |
color of the bias line. |
ci.area |
logical value. If |
ci.area.col |
color of the confidence area. |
ci.border |
logical value. If ci.border=TRUE the confidence limits will be drawn. |
ci.border.col |
color of the confidence limits. |
ci.border.lwd |
line width of confidence limits. |
ci.border.lty |
line type of confidence limits. |
zeroline |
logical value. If |
zeroline.col |
color of the zero-line. |
zeroline.lty |
type of the zero-line. |
zeroline.lwd |
width of the zero-line. |
main |
character string. The main title of plot. If |
sub |
character string. The subtitle of plot. If |
add.grid |
logical value. If |
xlab |
label for the x-axis |
ylab |
label for the y-axis |
cut.point |
numeric value. Decision level of interest. |
cut.point.col |
color of the confidence bounds at the required decision level. |
cut.point.lwd |
line width of the confidence bounds at the required decision level. |
cut.point.lty |
line type of the confidence bounds at the required decision level. |
... |
further graphical parameters |
No return value, instead a plot is generated
calcBias, plot.mcr, plotResiduals, plotDifference, compareFit
#library("mcrPioda") data(creatinine,package="mcrPioda") creatinine <- creatinine[complete.cases(creatinine),] x <- creatinine$serum.crea y <- creatinine$plasma.crea # Calculation of models m1 <- mcreg(x,y,method.reg="WDeming", method.ci="jackknife", mref.name="serum.crea",mtest.name="plasma.crea", na.rm=TRUE) m2 <- mcreg(x,y,method.reg="WDeming", method.ci="bootstrap", method.bootstrap.ci="BCa",mref.name="serum.crea", mtest.name="plasma.crea", na.rm=TRUE) # Grafical comparison of systematical Bias of two models plotBias(m1, zeroline=TRUE,zeroline.col="black",zeroline.lty=1, ci.area=TRUE,ci.border=FALSE, ci.area.col=grey(0.9), main = "Bias between serum and plasma creatinine", sub="Comparison of Jackknife and BCa-Bootstrap confidence bounds ") plotBias(m2, ci.area=FALSE, ci.border=TRUE, ci.border.lwd=2, ci.border.col="red",bias=FALSE ,add=TRUE) includeLegend(place="topleft",models=list(m1,m2), lwd=c(10,2), lty=c(2,1),colors=c(grey(0.9),"red"), bias=TRUE, design="1", digits=4) # Drawing of proportional bias plotBias(m1, ci.area=FALSE, ci.border=TRUE) plotBias(m1, ci.area=FALSE, ci.border=TRUE, prop=TRUE) plotBias(m1, ci.area=FALSE, ci.border=TRUE, prop=TRUE, cut.point=0.6) plotBias(m1, ci.area=FALSE, ci.border=TRUE, prop=TRUE, cut.point=0.6, xlim=c(0.4,0.8),cut.point.col="orange", cut.point.lwd=3, main ="")#library("mcrPioda") data(creatinine,package="mcrPioda") creatinine <- creatinine[complete.cases(creatinine),] x <- creatinine$serum.crea y <- creatinine$plasma.crea # Calculation of models m1 <- mcreg(x,y,method.reg="WDeming", method.ci="jackknife", mref.name="serum.crea",mtest.name="plasma.crea", na.rm=TRUE) m2 <- mcreg(x,y,method.reg="WDeming", method.ci="bootstrap", method.bootstrap.ci="BCa",mref.name="serum.crea", mtest.name="plasma.crea", na.rm=TRUE) # Grafical comparison of systematical Bias of two models plotBias(m1, zeroline=TRUE,zeroline.col="black",zeroline.lty=1, ci.area=TRUE,ci.border=FALSE, ci.area.col=grey(0.9), main = "Bias between serum and plasma creatinine", sub="Comparison of Jackknife and BCa-Bootstrap confidence bounds ") plotBias(m2, ci.area=FALSE, ci.border=TRUE, ci.border.lwd=2, ci.border.col="red",bias=FALSE ,add=TRUE) includeLegend(place="topleft",models=list(m1,m2), lwd=c(10,2), lty=c(2,1),colors=c(grey(0.9),"red"), bias=TRUE, design="1", digits=4) # Drawing of proportional bias plotBias(m1, ci.area=FALSE, ci.border=TRUE) plotBias(m1, ci.area=FALSE, ci.border=TRUE, prop=TRUE) plotBias(m1, ci.area=FALSE, ci.border=TRUE, prop=TRUE, cut.point=0.6) plotBias(m1, ci.area=FALSE, ci.border=TRUE, prop=TRUE, cut.point=0.6, xlim=c(0.4,0.8),cut.point.col="orange", cut.point.lwd=3, main ="")
Draw different Bland-Altman plot modifications (see parameter plot.type).
MCResult.plotDifference( .Object, xlab = NULL, ylab = NULL, ref.line = TRUE, ref.line.col = "black", ref.line.lty = 1, ref.line.lwd = 1, bias.line.lty = 1, bias.line.lwd = 1, bias.line.col = "red", bias.text.col = NULL, bias.text.cex = 0.8, loa.line.lty = 2, loa.line.lwd = 1, loa.line.col = "red", loa.text.col = NULL, plot.type = 3, main = NULL, cex = 0.8, digits = 2, add.grid = TRUE, ylim = NULL, ... )MCResult.plotDifference( .Object, xlab = NULL, ylab = NULL, ref.line = TRUE, ref.line.col = "black", ref.line.lty = 1, ref.line.lwd = 1, bias.line.lty = 1, bias.line.lwd = 1, bias.line.col = "red", bias.text.col = NULL, bias.text.cex = 0.8, loa.line.lty = 2, loa.line.lwd = 1, loa.line.col = "red", loa.text.col = NULL, plot.type = 3, main = NULL, cex = 0.8, digits = 2, add.grid = TRUE, ylim = NULL, ... )
.Object |
object of class "MCResult". |
xlab |
label for the x-axis |
ylab |
label for the y-axis |
ref.line |
logical value. If |
ref.line.col |
reference line color. |
ref.line.lty |
reference line type. |
ref.line.lwd |
reference line width. |
bias.line.lty |
line type for estimated bias. |
bias.line.lwd |
line width for estimated bias. |
bias.line.col |
color of the line for estimated bias. |
bias.text.col |
color of the label for estimated bias (defaults to the same as |
bias.text.cex |
The magnification to be used for the label for estimated bias |
loa.line.lty |
line type for estimated limits of agreement. |
loa.line.lwd |
line width for estimated limits of agreement. |
loa.line.col |
color of the line for estimated limits of agreement. |
loa.text.col |
color of the label for estimated limits of agreement (defaults to the same as |
plot.type |
integer specifying a specific Bland-Altman plot modification (default is 3).
Possible choices are:
1 - difference plot X vs. Y-X with null-line and mean plus confidence intervals. |
main |
plot title. |
cex |
numeric value specifying the magnification factor used for points |
digits |
number of decimal places for the difference of means and standard deviation appearing in the plot. |
add.grid |
logical value. If |
ylim |
limits for the y-axis |
... |
further graphical parameters |
No return value, instead a plot is generated
Bland, J. M., Altman, D. G. (1986) Statistical methods for assessing agreement between two methods of clinical measurement. Lancet, i: 307–310.
plot.mcr, plotResiduals, plotDifference, plotBias, compareFit
#library("mcrPioda") data(creatinine,package="mcrPioda") x <- creatinine$serum.crea y <- creatinine$plasma.crea # Deming regression fit. # The confidence intercals for regression coefficients # are calculated with analytical method model <- mcreg( x,y,error.ratio=1,method.reg="Deming", method.ci="analytical", mref.name = "serum.crea", mtest.name = "plasma.crea", na.rm=TRUE ) plotDifference( model ) # Default plot.type=3 plotDifference( model, plot.type=5) plotDifference( model, plot.type=7, ref.line.lty=3, ref.line.col="green3" )#library("mcrPioda") data(creatinine,package="mcrPioda") x <- creatinine$serum.crea y <- creatinine$plasma.crea # Deming regression fit. # The confidence intercals for regression coefficients # are calculated with analytical method model <- mcreg( x,y,error.ratio=1,method.reg="Deming", method.ci="analytical", mref.name = "serum.crea", mtest.name = "plasma.crea", na.rm=TRUE ) plotDifference( model ) # Default plot.type=3 plotDifference( model, plot.type=5) plotDifference( model, plot.type=7, ref.line.lty=3, ref.line.col="green3" )
Plot Residuals of an MCResult Object
MCResult.plotResiduals( .Object, res.type = c("optimized", "y", "x"), xaxis = c("yhat", "both", "xhat"), ref.line = TRUE, ref.line.col = "red", ref.line.lty = 2, ref.line.lwd = 1, main = NULL, xlab = NULL, ylab = NULL, add.grid = TRUE, ... )MCResult.plotResiduals( .Object, res.type = c("optimized", "y", "x"), xaxis = c("yhat", "both", "xhat"), ref.line = TRUE, ref.line.col = "red", ref.line.lty = 2, ref.line.lwd = 1, main = NULL, xlab = NULL, ylab = NULL, add.grid = TRUE, ... )
.Object |
object of type "MCResult". |
res.type |
If res.type="y" the difference between the test method and it's prediction will be drawn. If res.type="x" the reference method and it's prediction will be drawn. In case ordinary and weighted ordinary linear regression this difference will be zero. |
xaxis |
Values on the x-axis. One can choose from estimated values of x (xaxis= |
ref.line |
logical value. If |
ref.line.col |
reference line color. |
ref.line.lty |
reference line type. |
ref.line.lwd |
reference line width. |
main |
character string specifying the main title of the plot |
xlab |
label for the x-axis |
ylab |
label for the y-axis |
add.grid |
logical value. If |
... |
further graphical parameters |
No return value, instead a plot is generated
getResiduals, plot.mcr, plotDifference, plotBias, compareFit
data(creatinine,package="mcrPioda") x <- creatinine$serum.crea y <- creatinine$plasma.crea # Deming regression fit. # The confidence intercals for regression coefficients # are calculated with analytical method model <- mcreg( x,y,error.ratio=1,method.reg="WDeming", method.ci="jackknife", mref.name = "serum.crea", mtest.name = "plasma.crea", na.rm=TRUE ) plotResiduals(model, res.type="optimized", xaxis="both" ) plotResiduals(model, res.type="y", xaxis="yhat")data(creatinine,package="mcrPioda") x <- creatinine$serum.crea y <- creatinine$plasma.crea # Deming regression fit. # The confidence intercals for regression coefficients # are calculated with analytical method model <- mcreg( x,y,error.ratio=1,method.reg="WDeming", method.ci="jackknife", mref.name = "serum.crea", mtest.name = "plasma.crea", na.rm=TRUE ) plotResiduals(model, res.type="optimized", xaxis="both" ) plotResiduals(model, res.type="y", xaxis="yhat")
Print Summary of a Regression Analysis
MCResult.printSummary(.Object)MCResult.printSummary(.Object)
.Object |
object of type "MCResult". |
No return value
"MCResultAnalytical"
Result of a method comparison based on analytical methods for computing confidence intervals.
Object is typically created by a call to function mcreg.
Object can be directly constructed by calling newMCResultAnalytical or new("MCResultAnalytical", data, xmean, para, mnames, regmeth, cimeth, error.ratio, alpha, weight).
xmean:Object of class "numeric" ~~
data:Object of class "data.frame" ~~
para:Object of class "matrix" ~~
mnames:Object of class "character" ~~
regmeth:Object of class "character" ~~
cimeth:Object of class "character" ~~
error.ratio:Object of class "numeric" ~~
alpha:Object of class "numeric" ~~
weight:Object of class "numeric" ~~
Class "MCResult", directly.
signature(.Object = "MCResultAnalytical"): ...
signature(.Object = "MCResultAnalytical"): ...
signature(.Object = "MCResultAnalytical"): ...
Ekaterina Manuilova [email protected], Andre Schuetzenmeister [email protected], Fabian Model [email protected], Sergej Potapov [email protected]
showClass("MCResultAnalytical")showClass("MCResultAnalytical")
Calculate predicted values for given values of the reference-method.
MCResultAnalytical.calcResponse(.Object, x.levels, alpha = 0.05)MCResultAnalytical.calcResponse(.Object, x.levels, alpha = 0.05)
.Object |
object of class 'MCResultAnalytical' |
x.levels |
numeric vector specifying values of the reference method for which prediction should be made |
alpha |
significance level for confidence intervals |
matrix with predicted values with confidence intervals for given values of the reference-method.
Initialize Method for 'MCResultAnalytical' Objects.
MCResultAnalytical.initialize( .Object, data = data.frame(X = NA, Y = NA), xmean = 0, para = matrix(NA, ncol = 4, nrow = 2), mnames = c("unknown", "unknown"), regmeth = "unknown", cimeth = "analytical", error.ratio = 0, alpha = 0.05, weight = 1 )MCResultAnalytical.initialize( .Object, data = data.frame(X = NA, Y = NA), xmean = 0, para = matrix(NA, ncol = 4, nrow = 2), mnames = c("unknown", "unknown"), regmeth = "unknown", cimeth = "analytical", error.ratio = 0, alpha = 0.05, weight = 1 )
.Object |
object to be initialized |
data |
empty data.frame |
xmean |
mean value |
para |
empty coefficient matrix |
mnames |
empty method names vector |
regmeth |
string specifying the regression-method |
cimeth |
string specifying the confidence interval method |
error.ratio |
for deming regression |
alpha |
value specifying the 100(1-alpha)% confidence-level |
weight |
1 for each data point |
No return value
Function prints a summary of the regression-analysis for objects of class 'MCResultAnalytical'.
MCResultAnalytical.printSummary(.Object)MCResultAnalytical.printSummary(.Object)
.Object |
object of class 'MCResultAnalytical' |
No return value
"MCResultBCa"
Result of a method comparison with BCa-bootstrap based confidence intervals.
Object is typically created by a call to function mcreg.
Object can be directly constructed by calling newMCResultBCa or new("MCResultBCa", data, para, xmean, mnames, regmeth, cimeth, bootcimeth, alpha, glob.coef, glob.sigma, nsamples, nnested, B0jack, B1jack, B0, B1, MX, rng.seed, rng.kind, sigmaB0, sigmaB1, error.ratio, weight,robust.cov).
glob.sigma:Object of class "numeric" ~~
xmean:Object of class "numeric" ~~
nsamples:Object of class "numeric" ~~
nnested:Object of class "numeric" ~~
B0:Object of class "numeric" ~~
B1:Object of class "numeric" ~~
sigmaB0:Object of class "numeric" ~~
sigmaB1:Object of class "numeric" ~~
MX:Object of class "numeric" ~~
bootcimeth:Object of class "character" ~~
rng.seed:Object of class "numeric" ~~
rng.kind:Object of class "character" ~~
glob.coef:Object of class "numeric" ~~
B0jack:Object of class "numeric" ~~
B1jack:Object of class "numeric" ~~
data:Object of class "data.frame" ~~
para:Object of class "matrix" ~~
mnames:Object of class "character" ~~
regmeth:Object of class "character" ~~
cimeth:Object of class "character" ~~
error.ratio:Object of class "numeric" ~~
alpha:Object of class "numeric" ~~
weight:Object of class "numeric" ~~
robust.cov:Object of class "character" ~~
Class "MCResultJackknife", directly.
Class "MCResult", by class "MCResultJackknife", distance 2.
signature(.Object = "MCResultBCa"): ...
signature(.Object = "MCResultBCa"): ...
signature(.Object = "MCResultBCa"): ...
Ekaterina Manuilova [email protected], Andre Schuetzenmeister [email protected], Fabian Model [email protected], Sergej Potapov [email protected]
showClass("MCResultBCa")showClass("MCResultBCa")
Function computes the bootstrap summary for objects of class 'MCResultBCa'.
MCResultBCa.bootstrapSummary(.Object)MCResultBCa.bootstrapSummary(.Object)
.Object |
object of class 'MCResultBCa' |
matrix of bootstrap results
Calculate predicted values for given values of the reference-method.
MCResultBCa.calcResponse( .Object, x.levels, alpha = 0.05, bootcimeth = .Object@bootcimeth )MCResultBCa.calcResponse( .Object, x.levels, alpha = 0.05, bootcimeth = .Object@bootcimeth )
.Object |
object of class 'MCResultBCa' |
x.levels |
numeric vector specifying values of the reference method for which prediction should be made |
alpha |
significance level for confidence intervals |
bootcimeth |
character string specifying the method to be used for bootstrap confidence intervals |
matrix with predicted values with confidence intervals for given values of the reference-method.
Method initializes newly created objects of class 'MCResultBCa'.
MCResultBCa.initialize( .Object, data = data.frame(X = NA, Y = NA), para = matrix(NA, ncol = 4, nrow = 2), xmean = 0, mnames = c("unknown", "unknown"), regmeth = "unknown", cimeth = "unknown", bootcimeth = "unknown", alpha = 0.05, glob.coef = c(0, 0), glob.sigma = c(0, 0), nsamples = 0, nnested = 0, B0jack = 0, B1jack = 0, B0 = 0, B1 = 0, MX = 0, rng.seed = as.numeric(NA), rng.kind = "unknown", sigmaB0 = 0, sigmaB1 = 0, error.ratio = 0, weight = 1, robust.cov = "MCD" )MCResultBCa.initialize( .Object, data = data.frame(X = NA, Y = NA), para = matrix(NA, ncol = 4, nrow = 2), xmean = 0, mnames = c("unknown", "unknown"), regmeth = "unknown", cimeth = "unknown", bootcimeth = "unknown", alpha = 0.05, glob.coef = c(0, 0), glob.sigma = c(0, 0), nsamples = 0, nnested = 0, B0jack = 0, B1jack = 0, B0 = 0, B1 = 0, MX = 0, rng.seed = as.numeric(NA), rng.kind = "unknown", sigmaB0 = 0, sigmaB1 = 0, error.ratio = 0, weight = 1, robust.cov = "MCD" )
.Object |
object to be initialized |
data |
empty data.frame |
para |
empty coefficient matrix |
xmean |
0 for init-purpose |
mnames |
empty method names vector |
regmeth |
string specifying the regression-method |
cimeth |
string specifying the confidence interval method |
bootcimeth |
string specifying the method for bootstrap confidence intervals |
alpha |
value specifying the 100(1-alpha)% confidence-level |
glob.coef |
global coefficients |
glob.sigma |
global sd values for regression parameters |
nsamples |
number of samples for resampling |
nnested |
number of inner simulation for nested bootstrap |
B0jack |
jackknife intercpet |
B1jack |
jackknife slope |
B0 |
intercept |
B1 |
slope |
MX |
parameter |
rng.seed |
random number generator seed |
rng.kind |
type of the random number generator |
sigmaB0 |
SD for intercepts |
sigmaB1 |
SD for slopes |
error.ratio |
for deming regression |
weight |
1 for each data point |
robust.cov |
Robust covariance method for Box & Ellipses plot |
No return value
Plot distriblution of bootstrap coefficients (slope and intercept).
MCResultBCa.plotBootstrapCoefficients(.Object, breaks = 20, ...)MCResultBCa.plotBootstrapCoefficients(.Object, breaks = 20, ...)
.Object |
Object of class "MCResultBCa" |
breaks |
used in function 'hist' (see ?hist) |
... |
further graphical parameters |
No return value
Plot distriblution of bootstrap pivot T for slope and intercept and compare them with t(n-2) distribution.
MCResultBCa.plotBootstrapT(.Object, breaks = 20, ...)MCResultBCa.plotBootstrapT(.Object, breaks = 20, ...)
.Object |
Object of class "MCResultBCa". |
breaks |
Number of breaks in histogram. |
... |
further graphical parameters |
No return value
Plot distriblution of bootstrap coefficients (slope and intercept), the covariance robust ellipses (Donoho - Stahel method) and the box of the classical confidence intervals.
MCResultBCa.plotBoxEllipses(.Object, robust.cov = "MCD")MCResultBCa.plotBoxEllipses(.Object, robust.cov = "MCD")
.Object |
Object of class "MCResultBCa" |
robust.cov |
Robust covariance matrix method for ellipses.
There are three options: |
Functions prints a summary of the regression-analysis for objects of class 'MCResultBCa'.
MCResultBCa.printSummary(.Object)MCResultBCa.printSummary(.Object)
.Object |
object of class 'MCResultBCa' |
No return value
"MCResultJackknife"
Result of a method comparison with Jackknife based confidence intervals.
Object is typically created by a call to function mcreg.
Object can be directly constructed by calling newMCResultJackknife or new("MCResultJackknife", data, para, mnames, regmeth, cimeth, alpha, glob.coef, B0jack, B1jack, error.ratio, weight).
glob.coef:Object of class "numeric" ~~
B0jack:Object of class "numeric" ~~
B1jack:Object of class "numeric" ~~
data:Object of class "data.frame" ~~
para:Object of class "matrix" ~~
mnames:Object of class "character" ~~
regmeth:Object of class "character" ~~
cimeth:Object of class "character" ~~
error.ratio:Object of class "numeric" ~~
alpha:Object of class "numeric" ~~
weight:Object of class "numeric" ~~
Class "MCResult", directly.
signature(.Object = "MCResultJackknife"): ...
signature(.Object = "MCResultJackknife"): ...
signature(.Object = "MCResultJackknife"): ...
signature(.Object = "MCResultJackknife"): ...
signature(.Object = "MCResultJackknife"): ...
Ekaterina Manuilova [email protected], Andre Schuetzenmeister [email protected], Fabian Model [email protected], Sergej Potapov [email protected]
showClass("MCResultJackknife")showClass("MCResultJackknife")
Calculate predicted values for given values of the reference-method.
MCResultJackknife.calcResponse(.Object, x.levels, alpha = 0.05)MCResultJackknife.calcResponse(.Object, x.levels, alpha = 0.05)
.Object |
object of class 'MCResultJackknife' |
x.levels |
numeric vector specifying values of the reference method for which prediction should be made |
alpha |
significance level for confidence intervals |
matrix with predicted values with confidence intervals for given values of the reference-method.
Extracts the intercept value from objects of class 'MCResultJackknife'.
MCResultJackknife.getJackknifeIntercept(.Object)MCResultJackknife.getJackknifeIntercept(.Object)
.Object |
object of class 'MCResultJackknife' |
(numeric) jackknife-intercept
Extracts the slope value from objects of class 'MCResultJackknife'.
MCResultJackknife.getJackknifeSlope(.Object)MCResultJackknife.getJackknifeSlope(.Object)
.Object |
object of class 'MCResultJackknife' |
(numeric) jackknife-slope
Calculate jackknife mean, bias and standard error.
MCResultJackknife.getJackknifeStatistics(.Object)MCResultJackknife.getJackknifeStatistics(.Object)
.Object |
object of class "MCResultJackknife" or "MCResultResampling" |
table with jackknife mean, bias and standard error for intercept and slope.
Calculate the value of relative jackknife function for each observation.
MCResultJackknife.getRJIF(.Object)MCResultJackknife.getRJIF(.Object)
.Object |
object of class "MCResultJackknife" or "MCResultResampling". |
a list of the following elements:
slope |
numeric vector containing the values of relative jackknife function of slope. |
intercept |
numeric vector containing the values of relative jackknife function of intercept. |
Efron, B. (1990) Jackknife-After-Bootstrap Standard Errors and Influence Functions. Technical Report , N 134.
Method initializes newly created objects of class 'MCResultAnalytical'.
MCResultJackknife.initialize( .Object, data = data.frame(X = NA, Y = NA), para = matrix(NA, ncol = 4, nrow = 2), mnames = c("unknown", "unknown"), regmeth = "unknown", cimeth = "jackknife", alpha = 0.05, glob.coef = c(0, 0), B0jack = 0, B1jack = 0, error.ratio = 0, weight = 1 )MCResultJackknife.initialize( .Object, data = data.frame(X = NA, Y = NA), para = matrix(NA, ncol = 4, nrow = 2), mnames = c("unknown", "unknown"), regmeth = "unknown", cimeth = "jackknife", alpha = 0.05, glob.coef = c(0, 0), B0jack = 0, B1jack = 0, error.ratio = 0, weight = 1 )
.Object |
object to be initialized |
data |
empty data.frame |
para |
empty coefficient matrix |
mnames |
empty method names vector |
regmeth |
string specifying the regression-method |
cimeth |
string specifying the confidence interval method |
alpha |
value specifying the 100(1-alpha)% confidence-level |
glob.coef |
global coefficients |
B0jack |
jackknife intercepts |
B1jack |
jackknife slopes |
error.ratio |
for deming regression |
weight |
1 for each data point |
No return value
The function draws reference method vs. test method as scatter plot. Observations with high influence (relative jackknife influence function is greater than 2) are highlighted as red points.
MCResultJackknife.plotwithRJIF(.Object)MCResultJackknife.plotwithRJIF(.Object)
.Object |
object of class "MCResultJackknife" or "MCResultResampling" |
No return value
Efron, B. (1990) Jackknife-After-Bootstrap Standard Errors and Influence Functions. Technical Report , N 134.
#library("mcrPioda") data(creatinine,package="mcrPioda") x <- creatinine$serum.crea y <- creatinine$plasma.crea # Deming regression fit. # The confidence intervals for regression coefficients # are calculated with jackknife method model <- mcreg( x,y,error.ratio=1,method.reg="Deming", method.ci="jackknife", mref.name = "serum.crea", mtest.name = "plasma.crea", na.rm=TRUE ) plotwithRJIF(model)#library("mcrPioda") data(creatinine,package="mcrPioda") x <- creatinine$serum.crea y <- creatinine$plasma.crea # Deming regression fit. # The confidence intervals for regression coefficients # are calculated with jackknife method model <- mcreg( x,y,error.ratio=1,method.reg="Deming", method.ci="jackknife", mref.name = "serum.crea", mtest.name = "plasma.crea", na.rm=TRUE ) plotwithRJIF(model)
Functions prints a summary of the regression-analysis for objects of class 'MCResultJackknife'.
MCResultJackknife.printSummary(.Object)MCResultJackknife.printSummary(.Object)
.Object |
object of class 'MCResultJackknife' |
No return value
"MCResultResampling"
Result of a method comparison with resampling based confidence intervals.
Object is typically created by a call to function mcreg.
Object can be directly constructed by calling newMCResultResampling or new("MCResultResampling", data, para, xmean, mnames, regmeth, cimeth, bootcimeth, alpha, glob.coef, rng.seed, rng.kind, glob.sigma, nsamples, nnested, B0, B1, MX, sigmaB0, sigmaB1, error.ratio, weight,robust.cov).
glob.coef:Object of class "numeric" ~~
glob.sigma:Object of class "numeric" ~~
xmean:Object of class "numeric" ~~
nsamples:Object of class "numeric" ~~
nnested:Object of class "numeric" ~~
B0:Object of class "numeric" ~~
B1:Object of class "numeric" ~~
sigmaB0:Object of class "numeric" ~~
sigmaB1:Object of class "numeric" ~~
MX:Object of class "numeric" ~~
bootcimeth:Object of class "character" ~~
rng.seed:Object of class "numeric" ~~
rng.kind:Object of class "character" ~~
data:Object of class "data.frame" ~~
para:Object of class "matrix" ~~
mnames:Object of class "character" ~~
regmeth:Object of class "character" ~~
cimeth:Object of class "character" ~~
error.ratio:Object of class "numeric" ~~
alpha:Object of class "numeric" ~~
weight:Object of class "numeric" ~~
robust.cov:Object of class "character" ~~
Class "MCResult", directly.
signature(.Object = "MCResultResampling"): ...
signature(.Object = "MCResultResampling"): ...
signature(.Object = "MCResultResampling"): ...
Ekaterina Manuilova [email protected], Andre Schuetzenmeister [email protected], Fabian Model [email protected], Sergej Potapov [email protected]
showClass("MCResultResampling")showClass("MCResultResampling")
Function computes the bootstrap summary for objects of class 'MCResultResampling'.
MCResultResampling.bootstrapSummary(.Object)MCResultResampling.bootstrapSummary(.Object)
.Object |
object of class 'MCResultResampling' |
matrix of bootstrap results
Calculate predicted values for given values of the reference-method.
MCResultResampling.calcResponse( .Object, x.levels, alpha = 0.05, bootcimeth = .Object@bootcimeth )MCResultResampling.calcResponse( .Object, x.levels, alpha = 0.05, bootcimeth = .Object@bootcimeth )
.Object |
object of class 'MCResultResampling' |
x.levels |
numeric vector specifying values of the reference method for which prediction should be made |
alpha |
significance level for confidence intervals |
bootcimeth |
bootstrap confidence interval method to be used |
matrix with predicted values with confidence intervals for given values of the reference-method.
Method initializes newly created objects of class 'MCResultAnalytical'.
MCResultResampling.initialize( .Object, data = data.frame(X = NA, Y = NA), para = matrix(NA, ncol = 4, nrow = 2), xmean = 0, mnames = c("unknown", "unknown"), regmeth = "unknown", cimeth = "unknown", bootcimeth = "unknown", alpha = 0.05, glob.coef = c(0, 0), rng.seed = as.numeric(NA), rng.kind = "unknown", glob.sigma = c(0, 0), nsamples = 0, nnested = 0, B0 = 0, B1 = 0, MX = 0, sigmaB0 = 0, sigmaB1 = 0, error.ratio = 0, weight = 1, robust.cov = "MCD" )MCResultResampling.initialize( .Object, data = data.frame(X = NA, Y = NA), para = matrix(NA, ncol = 4, nrow = 2), xmean = 0, mnames = c("unknown", "unknown"), regmeth = "unknown", cimeth = "unknown", bootcimeth = "unknown", alpha = 0.05, glob.coef = c(0, 0), rng.seed = as.numeric(NA), rng.kind = "unknown", glob.sigma = c(0, 0), nsamples = 0, nnested = 0, B0 = 0, B1 = 0, MX = 0, sigmaB0 = 0, sigmaB1 = 0, error.ratio = 0, weight = 1, robust.cov = "MCD" )
.Object |
object to be initialized |
data |
empty data.frame |
para |
empty coefficient matrix |
xmean |
0 for init-purpose |
mnames |
empty method names vector |
regmeth |
string specifying the regression-method |
cimeth |
string specifying the confidence interval method |
bootcimeth |
string specifying the method for bootstrap confidence intervals |
alpha |
value specifying the 100(1-alpha)% confidence-level |
glob.coef |
global coefficients |
rng.seed |
random number generator seed |
rng.kind |
type of the random number generator |
glob.sigma |
global sd values for regression parameters |
nsamples |
number of samples for resampling |
nnested |
number of inner simulation for nested bootstrap |
B0 |
resampling intercepts |
B1 |
resampling slopes |
MX |
Numeric vector with point estimations of (weighted-)average of reference method values for each bootstrap sample. |
sigmaB0 |
SD for 'B0' |
sigmaB1 |
SD for 'B1' |
error.ratio |
for deming regression |
weight |
1 for each data point |
robust.cov |
Robust covariance method for Box & Ellipses plot |
No return value
Plot distriblution of bootstrap coefficients (slope and intercept).
MCResultResampling.plotBootstrapCoefficients(.Object, breaks = 20, ...)MCResultResampling.plotBootstrapCoefficients(.Object, breaks = 20, ...)
.Object |
Object of class "MCResultResampling" |
breaks |
see function 'hist' (?hist) for details |
... |
further graphical parameters |
No return value
Plot distriblution of bootstrap pivot T for slope and intercept and compare them with t(n-2) distribution.
MCResultResampling.plotBootstrapT(.Object, breaks = 20, ...)MCResultResampling.plotBootstrapT(.Object, breaks = 20, ...)
.Object |
Object of class "MCResultResampling". |
breaks |
Number of breaks in histogram. |
... |
further graphical parameters |
No return value
Plot distriblution of bootstrap coefficients (slope and intercept) with robust covariance ellipses and a CI box fot hypotesis testing.
MCResultResampling.plotBoxEllipses(.Object, robust.cov = "MCD")MCResultResampling.plotBoxEllipses(.Object, robust.cov = "MCD")
.Object |
Object of class "MCResultResampling" |
robust.cov |
Robust covariance matrix method for ellipses
There are three options: |
Functions prints a summary of the regression-analysis for objects of class 'MCResultResampling'.
MCResultResampling.printSummary(.Object)MCResultResampling.printSummary(.Object)
.Object |
object of class 'MCResultResampling' |
MCResult Object Constructor with Matrix in Wide Format as Input
newMCResult( wdata, para, sample.names = NULL, method.names = NULL, regmeth = "Unknown", cimeth, error.ratio, alpha = 0.05, weight = rep(1, nrow(wdata)) )newMCResult( wdata, para, sample.names = NULL, method.names = NULL, regmeth = "Unknown", cimeth, error.ratio, alpha = 0.05, weight = rep(1, nrow(wdata)) )
wdata |
measurement data in matrix format. First column reference method (x), second column test method (y). |
para |
regression parameters in matrix form. Rows: Intercept, Slope. Cols: EST, SE, LCI, UCI. |
sample.names |
names of individual data points, e.g. barcodes of measured samples. |
method.names |
names of reference and test method. |
regmeth |
name of statistical method used for regression. |
cimeth |
name of statistical method used for computing confidence intervals. |
error.ratio |
ratio between standard deviation of reference and test method. |
alpha |
numeric value specifying the 100(1- |
weight |
numeric vector specifying the weights used for each point |
MCResult object containing regression results.
MCResultAnalytical object constructor with matrix in wide format as input.
newMCResultAnalytical( wdata, para, xmean, sample.names = NULL, method.names = NULL, regmeth = "Unknown", cimeth = "analytical", error.ratio = error.ratio, alpha = 0.05, weight = rep(1, nrow(wdata)) )newMCResultAnalytical( wdata, para, xmean, sample.names = NULL, method.names = NULL, regmeth = "Unknown", cimeth = "analytical", error.ratio = error.ratio, alpha = 0.05, weight = rep(1, nrow(wdata)) )
wdata |
Measurement data in matrix format. First column reference method (x), second column comparator method (y). |
para |
Regression parameters in matrix form. Rows: Intercept, Slope. Cols: EST, SE, LCI, UCI. |
xmean |
Global (weighted) mean of x-values. |
sample.names |
Names of individual data points, e.g. barcodes of measured samples. |
method.names |
Names of reference and comparator method. |
regmeth |
Name of statistical method used for regression. |
cimeth |
Name of statistical method used for computing confidence intervals. |
error.ratio |
Ratio between standard deviation of reference and comparator method. |
alpha |
1 - significance level for confidence intervals. |
weight |
numeric vector specifying the weights used for each point |
MCResultAnalytical object containing regression results.
MCResultBCa object constructor with matrix in wide format as input.
newMCResultBCa( wdata, para, xmean, sample.names = NULL, method.names = NULL, regmeth = "unknown", glob.coef, glob.sigma, cimeth = "unknown", bootcimeth = "unknown", nsamples, nnested, rng.seed, rng.kind, B0jack, B1jack, B0, B1, MX, sigmaB0, sigmaB1, error.ratio, alpha = 0.05, weight = rep(1, nrow(wdata)) )newMCResultBCa( wdata, para, xmean, sample.names = NULL, method.names = NULL, regmeth = "unknown", glob.coef, glob.sigma, cimeth = "unknown", bootcimeth = "unknown", nsamples, nnested, rng.seed, rng.kind, B0jack, B1jack, B0, B1, MX, sigmaB0, sigmaB1, error.ratio, alpha = 0.05, weight = rep(1, nrow(wdata)) )
wdata |
Measurement data in matrix format. First column reference method (x), second column comparator method (y). |
para |
Regression parameters in matrix form. Rows: Intercept, Slope. Cols: EST, SE, LCI, UCI. |
xmean |
Global (weighted) mean of x-values |
sample.names |
Names of individual data points, e.g. barcodes of measured samples. |
method.names |
Names of reference and comparator method. |
regmeth |
Name of statistical method used for regression. |
glob.coef |
Numeric vector of length two with global point estimations of intercept and slope. |
glob.sigma |
Numeric vector of length two with global estimations of standard errors of intercept and slope. |
cimeth |
Name of statistical method used for computing confidence intervals. |
bootcimeth |
Bootstrap based confidence interval estimation method. |
nsamples |
Number of bootstrap samples. |
nnested |
Number of nested bootstrap samples. |
rng.seed |
Seed used to call mcreg, NULL if no seed was used |
rng.kind |
RNG type (string, see set.seed for details) used, only meaningfull if rng.seed was specified |
B0jack |
Numeric vector with point estimations of intercept for jackknife samples. |
B1jack |
Numeric vector with point estimations of slope for jackknife samples. |
B0 |
Numeric vector with point estimations of intercept for each bootstrap sample. |
B1 |
Numeric vector with point estimations of slope for each bootstrap sample. |
MX |
Numeric vector with point estimations of (weighted-)average of reference method values for each bootstrap sample. |
sigmaB0 |
Numeric vector with estimation of standard error of intercept for each bootstrap sample. |
sigmaB1 |
Numeric vector with estimation of standard error of slope for each bootstrap sample. |
error.ratio |
Ratio between standard deviation of reference and comparator method. |
alpha |
1 - significance level for confidence intervals. |
weight |
numeric vector specifying the weights used for each point |
MCResult object containing regression results.
MCResultJackknife Object Constructor with Matrix in Wide Format as Input
newMCResultJackknife( wdata, para, sample.names = NULL, method.names = NULL, regmeth = "Unknown", glob.coef, cimeth = "unknown", B0jack, B1jack, error.ratio = error.ratio, alpha = 0.05, weight = rep(1, nrow(wdata)) )newMCResultJackknife( wdata, para, sample.names = NULL, method.names = NULL, regmeth = "Unknown", glob.coef, cimeth = "unknown", B0jack, B1jack, error.ratio = error.ratio, alpha = 0.05, weight = rep(1, nrow(wdata)) )
wdata |
measurement data in matrix format. First column reference method (x), second column test method (y). |
para |
regression parameters in matrix form. Rows: Intercept, Slope. Cols: EST, SE, LCI, UCI. |
sample.names |
names of individual data points, e.g. barcodes of measured samples. |
method.names |
names of reference and test method. |
regmeth |
name of statistical method used for regression. |
glob.coef |
global coefficients |
cimeth |
name of statistical method used for computing confidence intervals. |
B0jack |
jackknife intercepts |
B1jack |
jeckknife slopes |
error.ratio |
ratio between standard deviation of reference and test method. |
alpha |
numeric value specifying the 100(1- |
weight |
numeric vector specifying the weights used for each point |
MCResult object containing regression results.
MCResultResampling object constructor with matrix in wide format as input.
newMCResultResampling( wdata, para, xmean, sample.names = NULL, method.names = NULL, regmeth = "unknown", glob.coef, glob.sigma, cimeth = "unknown", bootcimeth = "unknown", nsamples, nnested, rng.seed, rng.kind, B0, B1, MX, sigmaB0, sigmaB1, error.ratio, alpha = 0.05, weight = rep(1, nrow(wdata)) )newMCResultResampling( wdata, para, xmean, sample.names = NULL, method.names = NULL, regmeth = "unknown", glob.coef, glob.sigma, cimeth = "unknown", bootcimeth = "unknown", nsamples, nnested, rng.seed, rng.kind, B0, B1, MX, sigmaB0, sigmaB1, error.ratio, alpha = 0.05, weight = rep(1, nrow(wdata)) )
wdata |
Measurement data in matrix format. First column reference method (x), second column comparator method (y). |
para |
Regression parameters in matrix form. Rows: Intercept, Slope. Cols: EST, SE, LCI, UCI. |
xmean |
Global (weighted) mean of x-values |
sample.names |
Names of individual data points, e.g. barcodes of measured samples. |
method.names |
Names of reference and comparator method. |
regmeth |
Name of statistical method used for regression. |
glob.coef |
Numeric vector of length two with global point estimations of intercept and slope. |
glob.sigma |
Numeric vector of length two with global estimations of standard errors of intercept and slope. |
cimeth |
Name of statistical method used for computing confidence intervals. |
bootcimeth |
Bootstrap based confidence interval estimation method. |
nsamples |
Number of bootstrap samples. |
nnested |
Number of nested bootstrap samples. |
rng.seed |
Seed used to call mcreg, NULL if no seed was used |
rng.kind |
RNG type (string, see set.seed for details) used, only meaningfull if rng.seed was specified |
B0 |
Numeric vector with point estimations of intercept for each bootstrap sample. |
B1 |
Numeric vector with point estimations of slope for each bootstrap sample. |
MX |
Numeric vector with point estimations of (weighted-)average of reference method values for each bootstrap sample. |
sigmaB0 |
Numeric vector with estimation of standard error of intercept for each bootstrap sample. |
sigmaB1 |
Numeric vector with estimation of standard error of slope for each bootstrap sample. |
error.ratio |
Ratio between standard deviation of reference and comparator method. |
alpha |
1 - significance level for confidence intervals. |
weight |
numeric vector specifying the weights used for each point |
MCResult object containing regression results.