Another option is to modify panel.bpplot in the Hmisc package and specify
library(lattice)
bwplot(..., panel=mypanel)
Note that panel.bpplot will show the mean. It shows more quantiles than
a standard box plot so you get more than a 3-number summary.
If you show the mean and standard deviation you are assuming much
(especially symmetry) about the distribution you are trying to show.
Frank
S Ellison wrote:
boxplot itself is hardwired to produce the boxplot.stats list, and that
is not easy to change.
To get a different set of stats, you would need to do things in rwo
stages:
i) create a boxplot object of the type returned by boxplot, but using
your own stats
ii) call bxp on that object.
That's kind of tricky.
One comparatively simple alternative is to use the lattice package's
bwplot, and specify an alternate function for the stats parameter. You
have to write the alternate function, though. Here's one that would
probably do something like what you want; it is intended to deliver
boxes set to mean +-sd, outliers marked outside mean+-2.5sd by default
and whiskers set to the outermost of mean+-sd or outermost non-outlier
data.
Not that I'd recommend it, but it's entertaining writing it. With a bit
more wrapping, it could be used to generate a bxp-like object as well,
as per uwe's suggestion.
boxplot.norm<-function(x, do.conf=T, coef=1.5, do.out=T, p=0.05) {
xx <- x[!is.na(x)]
n <- length(xx)
s<-sd(xx)
m<-mean(xx)
stats <- c(min(xx), m-s, m, m+s, max(xx) )
if(coef == 0 ) do.out <- FALSE
#for compatibility with boxplot.stats
if (do.out) {
out <- abs(xx-mean(xx))/s > (coef+1)
#coef+1 gives outliers outside mean+-2.5s,
because bwplot
#passes its default coef=1.5 to the stats
function and outlier
#marking at 2.5s is not a million miles from
boxplot.stats's
#lower/upper quartiles -/+ 1.5*iqr if normality
is assumed
} else {
out <- numeric(0)
}
if (any(out))
stats[c(1, 5)] <- range(xx[!out])
#and tidy up any silly whiskers... mean+-sd can be outside the
outer data points
stats[1]<-min(stats[1:2])
stats[5]<-max(stats[4:5])
conf <- if (do.conf && n>1)
stats[3] + c(-1,1) * s * qt(1-p/2, n-1)/sqrt(n)
#Note: this is simply the (1-p)% confidence interval, not the
notch width
#required for a pairwise test at (1-p)% confidence. If notches
don't overlap, though,
#you certainly have a significant difference at _at least_ the
(1-p)% level.
#But bwplot can't use it anyway, 'cos it doesn't do notches.
list(stats = stats, n = n, conf = conf, out = xx[out] )
}
##Try it out...
require(lattice)
x<-rnorm(100)
g<-gl(5,20)
bwplot(x~g, main="The default")
windows()
bwplot(x~g, stats=boxplot.norm, main="Mean +- SD")
Chad Junkermeier <[EMAIL PROTECTED]> 05/08/2008 05:36 >>>
I really like the ease of use with the boxplot command in R. I would
rather have a boxplot that shows the average value and the standard
deviation then the median value and the quartiles.
Is there a way to do this?
Chad Junkermeier, Graduate Student
Dept. of Physics
West Virginia University
PO Box 6315
210 Hodges Hall
Morgantown WV 26506-6315
phone: (304) 293-3442 ext. 1430
fax: (304) 293-5732
email: chad.junkermeier{at}mail.wvu.edu
-----------------------------------------------------
Concurrently at:
Dept. of Physics and Astronomy
Brigham Young University
Provo UT 84602
email: junkermeier{at}byu.edu
cell: (801) 380-8895
--
Frank E Harrell Jr Professor and Chair School of Medicine
Department of Biostatistics Vanderbilt University
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