R: Systematical Bias Between Reference Method and Test Method

MCResult.calcBias {mcrPioda}

R Documentation

Systematical Bias Between Reference Method and Test Method

Description

Calculate systematical bias between reference and test methods at the decision point Xc as Bias(Xc) = Intercept + (Slope-1) * Xc with corresponding confidence intervals.

Usage

MCResult.calcBias(
  .Object,
  x.levels,
  type = c("absolute", "proportional"),
  percent = TRUE,
  alpha = 0.05,
  ...
)

Arguments

`.Object`	object of class "MCResult".
`x.levels`	a numeric vector with decision points for which bias should be calculated.
`type`	One can choose between absolute (default) and proportional bias (`Bias(Xc)/Xc`).
`percent`	logical value. If `percent = TRUE` the proportional bias will be calculated in percent.
`alpha`	numeric value specifying the 100(1-`alpha`)% confidence level of the confidence interval (Default is 0.05).
`...`	further parameters

Value

response and corresponding confidence interval for each decision point from x.levels.

Examples

    #library("mcr")
    data(creatinine,package="mcrPioda")
    x <- creatinine$serum.crea
    y <- creatinine$plasma.crea

    # Deming regression fit.
    # The confidence intervals for regression coefficients
    # are calculated with analytical method
    model <- mcreg( x,y,error.ratio = 1,method.reg = "Deming", method.ci = "analytical",

                     mref.name = "serum.crea", mtest.name = "plasma.crea", na.rm=TRUE )
    # Now we calculate the systematical bias
    # between the testmethod and the reference method
    # at the medical decision points 1, 2 and 3 

    calcBias( model, x.levels = c(1,2,3))
    calcBias( model, x.levels = c(1,2,3), type = "proportional")
    calcBias( model, x.levels = c(1,2,3), type = "proportional", percent = FALSE)