MCResult.plotBias {mcr}

Plot Estimated Systematical Bias with Confidence Bounds

Description

This function plots the estimated systematical bias ( Intercept + Slope * Refrencemethod ) - Referencemethod with confidence bounds, covering the whole range of reference method X or only part of it.

Usage

MCResult.plotBias(
  x,
  xn = 100,
  alpha = 0.05,
  add = FALSE,
  prop = FALSE,
  xlim = NULL,
  ylim = NULL,
  bias = TRUE,
  bias.lty = 1,
  bias.lwd = 2,
  bias.col = NULL,
  ci.area = TRUE,
  ci.area.col = NULL,
  ci.border = FALSE,
  ci.border.col = NULL,
  ci.border.lwd = 1,
  ci.border.lty = 2,
  zeroline = TRUE,
  zeroline.col = NULL,
  zeroline.lty = 2,
  zeroline.lwd = 1,
  main = NULL,
  sub = NULL,
  add.grid = TRUE,
  xlab = NULL,
  ylab = NULL,
  cut.point = NULL,
  cut.point.col = "red",
  cut.point.lwd = 2,
  cut.point.lty = 1,
  ...
)

Arguments

x

object of class "MCResult".

xn

# number of poits for drawing of confidence bounds/area.

alpha

numeric value specifying the 100(1-alpha)% confidence level of confidence intervals (Default is 0.05).

add

logical value. If add=TRUE, the grafic will be drawn in current grafical window.

prop

a logical value. If prop=TRUE the proportional bias \%bias(Xc) = [ Intercept + (Slope-1) * Xc ] / Xc will be drawn.

xlim

limits of the x-axis. If xlim=NULL the x-limits will be calculated automatically.

ylim

limits of the y-axis. If ylim=NULL the y-limits will be calculated automatically.

bias

logical value. If identity=TRUE the bias line will be drawn. If ci.bounds=FALSE and ci.area=FALSE the bias line will be drawn always.

bias.lty

type of the bias line.

bias.lwd

width of the bias line.

bias.col

color of the bias line.

ci.area

logical value. If ci.area=TRUE (default) the confidence area will be drawn.

ci.area.col

color of the confidence area.

ci.border

logical value. If ci.border=TRUE the confidence limits will be drawn.

ci.border.col

color of the confidence limits.

ci.border.lwd

line width of confidence limits.

ci.border.lty

line type of confidence limits.

zeroline

logical value. If zeroline=TRUE the zero-line will be drawn.

zeroline.col

color of the zero-line.

zeroline.lty

type of the zero-line.

zeroline.lwd

width of the zero-line.

main

character string. The main title of plot. If main = NULL it will include regression name.

sub

character string. The subtitle of plot. If sub=NULL and ci.border=TRUE or ci.area=TRUE it will include the art of confidence bounds calculation.

add.grid

logical value. If grid=TRUE (default) the gridlines will be drawn.

xlab

label for the x-axis

ylab

label for the y-axis

cut.point

numeric value. Decision level of interest.

cut.point.col

color of the confidence bounds at the required decision level.

cut.point.lwd

line width of the confidence bounds at the required decision level.

cut.point.lty

line type of the confidence bounds at the required decision level.

...

further graphical parameters

Value

No return value, instead a plot is generated

See Also

calcBias, plot.mcr, plotResiduals, plotDifference, compareFit

Examples

#library("mcr")
data(creatinine,package="mcr")

creatinine <- creatinine[complete.cases(creatinine),]
x <- creatinine$serum.crea
y <- creatinine$plasma.crea

# Calculation of models
m1 <- mcreg(x,y,method.reg="WDeming", method.ci="jackknife",
                mref.name="serum.crea",mtest.name="plasma.crea", na.rm=TRUE)
m2 <- mcreg(x,y,method.reg="WDeming", method.ci="bootstrap",
                method.bootstrap.ci="BCa",mref.name="serum.crea",
                mtest.name="plasma.crea", na.rm=TRUE)

# Grafical comparison of systematical Bias of two models
plotBias(m1, zeroline=TRUE,zeroline.col="black",zeroline.lty=1,
                ci.area=TRUE,ci.border=FALSE, ci.area.col=grey(0.9),
                main = "Bias between serum and plasma creatinine",
                sub="Comparison of Jackknife and BCa-Bootstrap confidence bounds ")
plotBias(m2, ci.area=FALSE, ci.border=TRUE, ci.border.lwd=2,
                ci.border.col="red",bias=FALSE ,add=TRUE)
includeLegend(place="topleft",models=list(m1,m2), lwd=c(10,2),
                lty=c(2,1),colors=c(grey(0.9),"red"), bias=TRUE,
                design="1", digits=4)

# Drawing of proportional bias
plotBias(m1, ci.area=FALSE, ci.border=TRUE)
plotBias(m1, ci.area=FALSE, ci.border=TRUE, prop=TRUE)
plotBias(m1, ci.area=FALSE, ci.border=TRUE, prop=TRUE, cut.point=0.6)
plotBias(m1, ci.area=FALSE, ci.border=TRUE, prop=TRUE, cut.point=0.6,
             xlim=c(0.4,0.8),cut.point.col="orange", cut.point.lwd=3, main ="")