Description Usage Arguments Value Author(s) See Also Examples
Produce BlandAltman Plot
BlandAltmanPlots for assessing agreement between two measuring methods or repeatability (testretest agreement) of measurements. Using either base graphics or ggplot2.
1 2 3 
group1 
Measurements with first method or first measurement 
group2 
Measurements with second method or second measurement 
two 
Lines are drawn "two" standard deviations from mean differences. This defaults to 1.96 for proper 95 percent confidence interval estimation but can be set to 2.0 for better agreement with e. g. the Bland Altman publication. 
mode 
if 1 then difference group1 minus group2 is used, if 2 then group2 minus group1 is used. Defaults to 1. 
graph.sys 
Graphing system within R. This defaults to "base" but can be
one out of 
conf.int 
Defaults to 0 which draws the usual Bland Altman plot which contains no confidence intervalls. Change to .95 for 95 percent confidence intervalls to be drawn. 
silent 
logical. If graph.sys=="base" and silent==TRUE then no return value. If graph.sys=="base" and silent==FALSE then returns statistics. 
sunflower 
logical. If TRUE, the plot will be based on a sunflower plot
and ties will be marked accordingly. Try with data with ties. Works only with

geom_count 
logical. If TRUE, the dots will get larger the more frequent
given pair is. Use in presence of ties. Works only with

... 
passed on to graphics functions if 
Depends on graphic system chosen. In case of "base" depending on whether
silent==TRUE. If silent==TRUE then no returns. If silent==FALSE than returns
list of statistics as returned by bland.altman.stats()
. In case the
graphics system is "ggplot2" than the graphic object is returned so that it
can be printed or altered.
Bernhard Lehnert <[email protected]>
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29  bland.altman.plot(rnorm(20), rnorm(20), xlab="mean measurement",
ylab="differences", main="Example plot")
bland.altman.plot(rnorm(20), 2+.8*rnorm(20), xlab="mean measurement",
ylab="differences", conf.int=.95)
bland.altman.plot(rnorm(200), 2+.8*rnorm(200), xlab="mean measurement",
ylab="differences", conf.int=.95)
# this is what fig.2 in Bland&Altman1986 would have looked like
PEFR1 < bland.altman.PEFR[,1]
PEFR2 < bland.altman.PEFR[,3]
bland.altman.plot(PEFR1, PEFR2, silent=TRUE, xlim=c(0,800),
xlab="Average PEFR by two meters",
ylab="Difference in PEFR (largemini)")
# and this is the same but with additional 95 percent CIs
data(bland.altman.PEFR)
bland.altman.plot(PEFR1, PEFR2, silent=TRUE, conf.int=.95, xlim=c(0,800))
# an example with many ties and the 'sunflower'option
a < rep(c(1,1,2,2,2,2,2,2,2,2,3,3,3,3,3,3,3,3,4,4,4,5,6,6),2)
b < rep(c(1,1,1,2,2,2,3,1,4,2,5,3,3,3,3,3),3)
bland.altman.plot(a,b,sunflower=TRUE, xlab="Mean",ylab="Difference",
main="discrete values lead to ties")
library(ggplot2)
a < bland.altman.plot(rnorm(20), rnorm(20), graph.sys="ggplot2", conf.int=.9)
print(a + xlab("you can change this later") + ggtitle("Title goes here"))

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