panel.bpplot: Box-Percentile Panel Function for Trellis

Description

For all their good points, box plots have a high ink/information ratio in that they mainly display 3 quartiles. Many practitioners have found that the "outer values" are difficult to explain to non-statisticians and many feel that the notion of "outliers" is too dependent on (false) expectations that data distributions should be Gaussian.

panel.bpplot is a panel function for use with trellis, especially for bwplot. It draws box plots (without the whiskers) with any number of user-specified "corners" (corresponding to different quantiles), but it also draws box-percentile plots similar to those drawn by Jeffrey Banfield's (umsfjban@bill.oscs.montana.edu) bpplot function. To quote from Banfield, "box-percentile plots supply more information about the univariate distributions. At any height the width of the irregular 'box' is proportional to the percentile of that height, up to the 50th percentile, and above the 50th percentile the width is proportional to 100 minus the percentile. Thus, the width at any given height is proportional to the percent of observations that are more extreme in that direction. As in boxplots, the median, 25th and 75th percentiles are marked with line segments across the box."

panel.bpplot is a generalization of bpplot and panel.bwplot in that it works with trellis (making the plots horizontal so that category labels are more visable), it allows the user to specify the quantiles to connect and those for which to draw reference lines, and it displays means (by default using dots).

bpplt draws horizontal box-percentile plot much like those drawn by panel.bpplot but taking as the starting point a matrix containing quantiles summarizing the data. bpplt is primarily intended to be used internally by plot.summary.formula.reverse but when used with no arguments has a general purpose: to draw an annotated example box-percentile plot with the default quantiles used and with the mean drawn with a solid dot. This schematic plot is rendered nicely in postscript with an image height of 3.5 inches.

Usage

panel.bpplot(x, y, box.ratio=1, means=TRUE, qref=c(.5,.25,.75),
             probs=c(.05,.125,.25,.375), nout=0,
             datadensity=FALSE, scat1d.opts=NULL,
             font=box.dot$font, pch=box.dot$pch, 
             cex =box.dot$cex,  col=box.dot$col, ...)
# E.g. bwplot(formula, panel=panel.bpplot, panel.bpplot.parameters)
bpplt(stats, xlim, xlab='', box.ratio = 1, means=TRUE,
      qref=c(.5,.25,.75), qomit=c(.025,.975),
      pch=16, cex.labels=par('cex'), cex.points=if(prototype)1 else 0.5,
      grid=FALSE)

Arguments

continuous variable whose distribution is to be examined

grouping variable

box.ratio

see panel.bwplot

means

set to FALSE to suppress drawing a character at the mean value

qref

vector of quantiles for which to draw reference lines. These do not need to be included in probs.

probs

vector of quantiles to display in the box plot. These should all be less than 0.5; the mirror-image quantiles are added automatically. By default, probs is set to c(.05,.125,.25,.375) so that intervals contain 0.9, 0.75, 0.5, a

nout

tells the function to use scat1d to draw tick marks showing the nout smallest and nout largest values if nout >= 1, or to show all values less than the nout quantile or greater than the

datadensity

set to FALSE to invoke scat1d to draw a data density (one-dimensional scatter diagram or rug plot) inside each box plot.

scat1d.opts

a list containing named arguments (without abbreviations) to pass to scat1d when datadensity=TRUE or nout > 0

font

pch

cex

col

see panel.bwplot

...

arguments passed to points

stats

xlim

xlab

qomit

cex.labels

cex.points

grid

undocumented arguments to bpplt

concept

trellis
lattice

References

Esty, W. W. and Banfield, J. D. (1992) "The Box-Percentile Plot," Technical Report (May 15, 1992), Department of Mathematical Sciences, Montana State University.

Examples

Run this code

set.seed(13)
x <- rnorm(1000)
g <- sample(1:6, 1000, replace=TRUE)
x[g==1][1:20] <- rnorm(20)+3   # contaminate 20 x's for group 1


# default trellis box plot
if(.R.) library(lattice)
bwplot(g ~ x)


# box-percentile plot with data density (rug plot)
bwplot(g ~ x, panel=panel.bpplot, probs=seq(.01,.49,by=.01), datadensity=TRUE)
# add ,scat1d.opts=list(tfrac=1) to make all tick marks the same size
# when a group has > 125 observations


# small dot for means, show only .05,.125,.25,.375,.625,.75,.875,.95 quantiles
bwplot(g ~ x, panel=panel.bpplot, cex=.3)


# suppress means and reference lines for lower and upper quartiles
bwplot(g ~ x, panel=panel.bpplot, probs=c(.025,.1,.25), means=FALSE, qref=FALSE)


# continuous plot up until quartiles ("Tootsie Roll plot")
bwplot(g ~ x, panel=panel.bpplot, probs=seq(.01,.25,by=.01))


# start at quartiles then make it continuous ("coffin plot")
bwplot(g ~ x, panel=panel.bpplot, probs=seq(.25,.49,by=.01))


# same as previous but add a spike to give 0.95 interval
bwplot(g ~ x, panel=panel.bpplot, probs=c(.025,seq(.25,.49,by=.01)))


# decile plot with reference lines at outer quintiles and median
bwplot(g ~ x, panel=panel.bpplot, probs=c(.1,.2,.3,.4), qref=c(.5,.2,.8))


# default plot with tick marks showing all observations outside the outer
# box (.05 and .95 quantiles), with very small ticks
bwplot(g ~ x, panel=panel.bpplot, nout=.05, scat1d.opts=list(frac=.01))


# show 5 smallest and 5 largest observations
bwplot(g ~ x, panel=panel.bpplot, nout=5)


# Use a scat1d option (preserve=TRUE) to ensure that the right peak extends 
# to the same position as the extreme scat1d
bwplot(~x , panel=panel.bpplot, probs=seq(.00,.5,by=.001), 
       datadensity=TRUE, scat1d.opt=list(preserve=TRUE))

# Draw a prototype showing how to interpret the plots
bpplt()

# make a local copy of bwplot that always uses panel.bpplot (S-Plus only)
# bwplot$panel <- panel.bpplot
# bwplot(g ~ x, nout=.05)

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