Hmisc (version 5.3-1)

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 ([email protected]) 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 can also be used with base graphics to add extended box plots to an existing plot, by specifying nogrid=TRUE, height=....

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 or plot.summaryM 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.

bppltp is like bpplt but for plotly graphics, and it does not draw an annotated extended box plot example.

bpplotM uses the lattice bwplot function to depict multiple numeric continuous variables with varying scales in a single lattice graph, after reshaping the dataset into a tall and thin format.

Usage

panel.bpplot(x, y, box.ratio=1, means=TRUE, qref=c(.5,.25,.75),
             probs=c(.05,.125,.25,.375), nout=0,
             nloc=c('right lower', 'right', 'left', 'none'), cex.n=.7,
             datadensity=FALSE, scat1d.opts=NULL,
             violin=FALSE, violin.opts=NULL,
             font=box.dot$font, pch=box.dot$pch, 
             cex.means =box.dot$cex,  col=box.dot$col,
             nogrid=NULL, height=NULL, ...)
# 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)
bppltp(p=plotly::plot_ly(),
       stats, xlim, xlab='', box.ratio = 1, means=TRUE,
       qref=c(.5,.25,.75), qomit=c(.025,.975),
       teststat=NULL, showlegend=TRUE)
bpplotM(formula=NULL, groups=NULL, data=NULL, subset=NULL, na.action=NULL,
        qlim=0.01, xlim=NULL,
        nloc=c('right lower','right','left','none'),
        vnames=c('labels', 'names'), cex.n=.7, cex.strip=1,
        outerlabels=TRUE, ...)

References

Esty WW, Banfield J: The box-percentile plot. J Statistical Software 8 No. 17, 2003.

See Also

bpplot, panel.bwplot, scat1d, quantile, Ecdf, summaryP, useOuterStrips

Examples

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
require(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.means=.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))

# Add an extended box plot to an existing base graphics plot
plot(x, 1:length(x))
panel.bpplot(x, 1070, nogrid=TRUE, pch=19, height=15, cex.means=.5)

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

# Example for bpplotM
set.seed(1)
n <- 800
d <- data.frame(treatment=sample(c('a','b'), n, TRUE),
                sex=sample(c('female','male'), n, TRUE),
                age=rnorm(n, 40, 10),
                bp =rnorm(n, 120, 12),
                wt =rnorm(n, 190, 30))
label(d$bp) <- 'Systolic Blood Pressure'
units(d$bp) <- 'mmHg'
bpplotM(age + bp + wt ~ treatment, data=d)
bpplotM(age + bp + wt ~ treatment * sex, data=d, cex.strip=.8)
bpplotM(age + bp + wt ~ treatment*sex, data=d,
        violin=TRUE,
        violin.opts=list(col=adjustcolor('blue', alpha.f=.15),
                         border=FALSE))


bpplotM(c('age', 'bp', 'wt'), groups='treatment', data=d)
# Can use Hmisc Cs function, e.g. Cs(age, bp, wt)
bpplotM(age + bp + wt ~ treatment, data=d, nloc='left')

# Without treatment: bpplotM(age + bp + wt ~ 1, data=d)

if (FALSE) {
# Automatically find all variables that appear to be continuous
getHdata(support)
bpplotM(data=support, group='dzgroup',
        cex.strip=.4, cex.means=.3, cex.n=.45)

# Separate displays for categorical vs. continuous baseline variables
getHdata(pbc)
pbc <- upData(pbc, moveUnits=TRUE)

s <- summaryM(stage + sex + spiders ~ drug, data=pbc)
plot(s)
Key(0, .5)
s <- summaryP(stage + sex + spiders ~ drug, data=pbc)
plot(s, val ~ freq | var, groups='drug', pch=1:3, col=1:3,
     key=list(x=.6, y=.8))

bpplotM(bili + albumin + protime + age ~ drug, data=pbc)
}