mcr (version 1.2.1)

MCResult.calcBias: 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".

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.

x.levels

a numeric vector with decision points for which bias schould be calculated.

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.

See Also

plotBias

Examples

# NOT RUN {
#library("mcr")
    data(creatinine,package="mcr")
    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)
# }