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MethComp (version 1.22.2)

plot.MCmcmc: Plot estimated conversion lines and formulae.

Description

Plots the pairwise conversion formulae between methods from a MCmcmc object.

Usage

"plot"( x, axlim = range( attr(x,"data")$y, na.rm=TRUE ), wh.cmp, lwd.line = c(3,1), col.line = rep("black",2), lty.line=rep(1,2), eqn = TRUE, digits = 2, grid = FALSE, col.grid=gray(0.8), points = FALSE, col.pts = "black", pch.pts = 16, cex.pts = 0.8, ... )

Arguments

x
A MCmcmc object
axlim
The limits for the axes in the panels
wh.cmp
Numeric vector or vector of method names. Which of the methods should be included in the plot?
lwd.line
Numerical vector of length 2. The width of the conversion line and the prediction limits. If the second values is 0, no prediction limits are drawn.
col.line
Numerical vector of length 2. The color of the conversion line and the prediction limits.
lty.line
Numerical vector of length 2. The line types of the conversion line and the prediction limits.
eqn
Should the conversion equations be printed on the plot?. Defaults to TRUE.
digits
How many digits after the decimal point shoudl be used when printing the conversion equations.
grid
Should a grid be drawn? If a numerical vector is given, the grid is drawn at those values.
col.grid
What color should the grid have?
points
Logical or character. Should the points be plotted. If TRUE or "repl" paired values of single replicates are plotted. If "perm", replicates are randomly permuted within (item, method) befor plotting. If "mean", means across replicates within item, method are formed and plotted.
col.pts
What color should the observation have.
pch.pts
What plotting symbol should be used.
cex.pts
What scaling should be used for the plot symbols.
...
Parameters to pass on. Currently not used.

Value

Nothing. The lower part of a (M-1) by (M-1) matrix of plots is drawn, showing the pairwise conversion lines. In the corners of each is given the two conversion equations together with the prediction standard error.

See Also

MCmcmc, print.MCmcmc

Examples

Run this code
## Not run: data( hba1c )
## Not run: str( hba1c )
## Not run: hba1c <- transform( subset( hba1c, type=="Ven" ),
#                     meth = dev,
#                     repl = d.ana )## End(Not run)
## Not run: hb.res <- MCmcmc( hba1c, n.iter=50 )
## Not run: data( hba.MC )
## Not run: str( hba.MC )
## Not run: par( ask=TRUE )
## Not run: plot( hba.MC )
## Not run: plot( hba.MC, pl.obs=TRUE )

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