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)))Measurement data in matrix format. First column reference method (x), second column comparator method (y).
Regression parameters in matrix form. Rows: Intercept, Slope. Cols: EST, SE, LCI, UCI.
Global (weighted) mean of x-values
Names of individual data points, e.g. barcodes of measured samples.
Names of reference and comparator method.
Name of statistical method used for regression.
Name of statistical method used for computing confidence intervals.
Ratio between standard deviation of reference and comparator method.
1 - significance level for confidence intervals.
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.
Bootstrap based confidence interval estimation method.
Number of bootstrap samples.
Number of nested bootstrap samples.
Seed used to call mcreg, NULL if no seed was used
RNG type (string, see set.seed for details) used, only meaningfull if rng.seed was specified
Numeric vector with point estimations of intercept for each bootstrap sample.
Numeric vector with point estimations of slope for each bootstrap sample.
Numeric vector with estimation of standard error of intercept for each bootstrap sample.
Numeric vector with estimation of standard error of slope for each bootstrap sample.
Numeric vector with point estimations of (weighted-)average of reference method values for each bootstrap sample.
numeric vector specifying the weights used for each point
MCResult object containing regression results.