newMCResultResampling {mcrPioda}

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 meaningful 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.