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"), X, Y, error.ratio, nsamples = 1000, jackknife = TRUE,
bootstrap = c("none", "bootstrap", "nestedbootstrap"), nnested = 25,
iter.max = 30, threshold = 1e-08, NBins = 1e+06,
slope.measure = c("radian", "tangent"))Measurement values of reference method
Measurement values of test method
Ratio between squared measurement errors of reference- and test method, necessary for Deming regression. Default 1.
Regression method. It is possible to
choose between five regression types: "LinReg" -
ordinary least square regression, "WLinReg" -
weighted ordinary least square regression,"Deming"
- Deming regression, "WDeming" - weighted Deming
regression, "PaBa" - Passing-Bablok regression.
Bootstrap based confidence interval estimation method.
Logical value. If TRUE - Jackknife based confidence interval estimation method.
Number of bootstrap samples.
Number of nested bootstrap samples.
maximum number of iterations for weighted Deming iterative algorithm.
Numerical tolerance for weighted Deming iterative algorithm convergence.
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.
angular measure of pairwise slopes
used for exact PaBa regression (see mcreg
for details). "radian" - for data sets with even
sample numbers median slope is calculated as average of
two central slope angles. "tangent" - for data sets
with even sample numbers median slope is calculated as
average of two central slopes (tan(angle)).
a list consisting of
Numeric vector of length two with global point estimations of intercept and slope.
Numeric vector of length two with global estimations of standard errors of intercept and slope.
Global (weighted-)average of reference method values.
Numeric vector with point estimations of intercept for jackknife samples. The i-th element contains point estimation for data set without i-th observation
Numeric vector with point estimations of slope for jackknife samples. The i-th element contains point estimation for data set without i-th observation
Numeric vector with point estimations of intercept for each bootstrap sample. The i-th element contains point estimation for i-th bootstrap sample.
Numeric vector with point estimations of slope for each bootstrap sample. The i-th element contains point estimation for i-th bootstrap sample.
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.
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.
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.
Number of bootstrap samples.
Number of nested bootstrap samples.
Method of confidence interval calculation (bootstrap).
Number of observations.
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.