panel.bpplot {Hmisc} | R Documentation

## 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` 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` 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.

`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)

    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, ...)

### Arguments

`x` | continuous variable whose distribution is to be examined
`y` | 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, and 0.25 of the data. To draw all 99 percentiles, i.e., to draw a box-percentile plot, set `probs=seq(.01,.49,by=.01)`. To make a more traditional box plot, use `probs=.25`.

`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 `1-nout` quantile if `0 < nout <= 0.5`. If `nout` is a whole number, only the first `n/2` observations are shown on either side of the median, where `n` is the total number of observations.
`nloc` | location to plot number of non-`NA` observations next to each box. Specify `nloc='none'` to suppress. For `panel.bpplot`, the default `nloc` is `'none'` if `nogrid=TRUE`.
`cex.n` | character size for `nloc`
`datadensity` | set to `TRUE` 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`
`violin` | set to `TRUE` to invoke `panel.violin` in addition to drawing box-percentile plots
`violin.opts` | a list of options to pass to `panel.violin`
`cex.means` | character size for dots representing means
`font,pch,col` | see `panel.bwplot`
`nogrid` | set to `TRUE` to use in base graphics
`height` | if `nogrid=TRUE`, specifies the height of the box in user `y` units
`...` | arguments passed to `points` or `panel.bpplot` or `bwplot`
`stats,xlim,xlab,qomit,cex.labels,cex.points,grid` | undocumented arguments to `bpplt`. For `bpplotM`, `xlim` is a list with elements named as the `x`-axis variables, to override the `qlim` calculations with user-specified `x`-axis limits for selected variables. Example: `xlim=list(age=c(20,60))`.

`formula`

a formula with continuous numeric analysis variables on the left hand side and stratification variables on the right. The first variable on the right is the one that will vary the fastest, forming the `y`-axis. `formula` may be omitted, in which case all numeric variables with more than 5 unique values in `data` will be analyzed. Or `formula` may be a vector of variable names in `data` to analyze. In the latter two cases (and only those cases), `groups` must be given, representing a character vector with names of stratification variables.

`groups` | see above
`data` | an optional data frame
`subset` | an optional subsetting expression or logical vector
`na.action` | specifies a function to possibly subset the data according to `NA`s (default is no such subsetting).
`qlim` | the outer quantiles to use for scaling each panel in `bpplotM`
`vnames` | default is to use variable `label` attributes when they exist, or use variable names otherwise. Specify `vnames='names'` to always use variable names for panel labels in `bpplotM`
`cex.strip` | character size for panel strip labels
`outerlabels` | if `TRUE`, pass the `lattice` graphics through the `latticeExtra` package's `useOuterStrips` function if there are two conditioning (paneling) variables, to put panel labels in outer margins.

### Author(s)

Frank Harrell
Department of Biostatistics
Vanderbilt University School of Medicine
f.harrell@vanderbilt.edu

### 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)

    ## Not run:
    # 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)

    ## End(Not run)

[Package Hmisc version 3.17-4 Index]