Systematical Bias Between Reference Method and Test Method

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

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 schould 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.

See also

plotBias

Examples

    #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 )
#> Please note: 
#> 2 of 110 observations contain missing values and have been removed.
#> Number of data points in analysis is 108.
    # 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))
#>    Level         Bias         SE         LCI       UCI
#> X1     1 -0.004374069 0.01784350 -0.03975054 0.0310024
#> X2     2  0.050165272 0.03186142 -0.01300309 0.1133336
#> X3     3  0.104704613 0.06488644 -0.02393907 0.2333483
    calcBias( model, x.levels = c(1,2,3), type = "proportional")
#>    Level Prop.bias(%)       SE        LCI      UCI
#> X1     1   -0.4374069 1.784350 -3.9750541 3.100240
#> X2     2    2.5082636 1.593071 -0.6501545 5.666682
#> X3     3    3.4901538 2.162881 -0.7979691 7.778277
    calcBias( model, x.levels = c(1,2,3), type = "proportional", percent = FALSE)
#>    Level    Prop.bias         SE          LCI        UCI
#> X1     1 -0.004374069 0.01784350 -0.039750541 0.03100240
#> X2     2  0.025082636 0.01593071 -0.006501545 0.05666682
#> X3     3  0.034901538 0.02162881 -0.007979691 0.07778277