newMCResultResampling: MCResultResampling object constructor with matrix in wide...

In mcr: Method Comparison Regression

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))
)

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

Value

MCResult object containing regression results.

mcr documentation built on Oct. 11, 2023, 5:14 p.m.