newMCResultBCa: MCResultBCa object constructor with matrix in wide format as...
In mcr: Method Comparison Regression

newMCResultBCa

R Documentation

MCResultBCa object constructor with matrix in wide format as input.

Description

MCResultBCa object constructor with matrix in wide format as input.

Usage

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))
)

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
`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