newMCResultResampling: MCResultResampling object constructor with matrix in wide format as input.

Description

MCResultResampling object constructor with matrix in wide format as input.

Usage

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))
)

Value

MCResult object containing regression results.

Arguments

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