Scatter Plot Method X vs. Method Y

Plot method X (reference) vs. method Y (test) with (optional) line of identity, regression line and confidence bounds for response.

MCResult.plot(
  x,
  alpha = 0.05,
  xn = 20,
  equal.axis = FALSE,
  xlim = NULL,
  ylim = NULL,
  xaxp = NULL,
  yaxp = NULL,
  x.lab = x@mnames[1],
  y.lab = x@mnames[2],
  add = FALSE,
  draw.points = TRUE,
  points.col = "black",
  points.pch = 1,
  points.cex = 0.8,
  reg = TRUE,
  reg.col = NULL,
  reg.lty = 1,
  reg.lwd = 2,
  identity = TRUE,
  identity.col = NULL,
  identity.lty = 2,
  identity.lwd = 1,
  ci.area = TRUE,
  ci.area.col = NULL,
  ci.border = FALSE,
  ci.border.col = NULL,
  ci.border.lty = 2,
  ci.border.lwd = 1,
  add.legend = TRUE,
  legend.place = c("topleft", "topright", "bottomleft", "bottomright"),
  main = NULL,
  sub = NULL,
  add.cor = TRUE,
  cor.method = c("pearson", "kendall", "spearman"),
  add.grid = TRUE,
  digits = list(coef = 2, cor = 3),
  ...
)

Arguments

x: object of class "MCResult".
alpha: numeric value specifying the 100(1-alpha)% confidence bounds.
xn: number of points (default 20) for calculation of confidence bounds.
equal.axis: logical value. If equal.axis=TRUE x-axis will be equal to y-axis.
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.
xaxp: ticks of the x-axis. If xaxp=NULL the x-ticks will be calculated automatically.
yaxp: ticks of the y-axis. If yaxp=NULL the y-ticks will be calculated automatically.
x.lab: label of x-axis. Default is the name of reference method.
y.lab: label of y-axis. Default is the name of test method.
add: logical value. If add=TRUE, the plot will be drawn in current graphical window.
draw.points: logical value. If draw.points=TRUE, the data points will be drawn.
points.col: Color of data points.
points.pch: Type of data points (see par()).
points.cex: Size of data points (see par()).
reg: Logical value. If reg=TRUE, the regression line will be drawn.
reg.col: Color of regression line.
reg.lty: Type of regression line.
reg.lwd: The width of regression line.
identity: logical value. If identity=TRUE the identity line will be drawn.
identity.col: The color of identity line.
identity.lty: The type of identity line.
identity.lwd: the width of identity line.
ci.area: logical value. If ci.area=TRUE (default) the confidence area will be drawn.
ci.area.col: the color of confidence area.
ci.border: logical value. If ci.border=TRUE the confidence limits will be drawn.
ci.border.col: The color of confidence limits.
ci.border.lty: The line type of confidence limits.
ci.border.lwd: The line width of confidence limits.
add.legend: logical value. If add.legend=FALSE the plot will not have any legend.
legend.place: The position of legend: "topleft","topright","bottomleft","bottomright".
main: String value. The main title of plot. If main=NULL it will include regression name.
sub: String value. 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.cor: Logical value. If add.cor=TRUE the correlation coefficient will be shown.
cor.method: a character string indicating which correlation coefficient is to be computed. One of "pearson" (default), "kendall", or "spearman", can be abbreviated.
add.grid: Logical value. If add.grid=TRUE (default) the gridlines will be drawn.
digits: list with the number of digits for the regression equation and the correlation coefficient.
...: further graphical parameters

Value

No return value, instead a plot is generated

Examples

 library(mcr)
 data(creatinine,package="mcr")
 creatinine <- creatinine[complete.cases(creatinine),]
  x <- creatinine$serum.crea
  y <- creatinine$plasma.crea

  m1 <- mcreg(x,y,method.reg="Deming",  mref.name="serum.crea",
                                        mtest.name="plasma.crea", na.rm=TRUE)
  m2 <- mcreg(x,y,method.reg="WDeming", method.ci="jackknife",
                                        mref.name="serum.crea",
                                        mtest.name="plasma.crea", na.rm=TRUE)
#> Jackknife based calculation of standard error and confidence intervals according to Linnet's method.

  plot(m1,  xlim=c(0.5,3),ylim=c(0.5,3), add.legend=FALSE,
                           main="Deming vs. weighted Deming regression",
                           points.pch=19,ci.area=TRUE, ci.area.col=grey(0.9),
                           identity=FALSE, add.grid=FALSE, sub="")
  plot(m2, ci.area=FALSE, ci.border=TRUE, ci.border.col="red3",
                           reg.col="red3", add.legend=FALSE,
                           draw.points=FALSE,add=TRUE)

  includeLegend(place="topleft",models=list(m1,m2),
                           colors=c("darkblue","red"), design="1", digits=2)

Arguments

Value

See also

Examples