# MCResult.calcBias: Systematical Bias Between Reference Method and Test Method In mcr: Method Comparison Regression

 MCResult.calcBias 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 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.

`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 )
# 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)
``````

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