Multiple left-hand formula variables along with right-hand side
conditioning variables are reshaped into a "tall and thin" data frame if
fun is not specified. The resulting raw data can be plotted with
the plot method using user-specified panel functions for
lattice graphics, typically to make a scatterplot or loess
smooths, or both. The Hmisc panel.plsmo function is handy
in this context. Instead, if fun is specified, this function
takes individual response variables (which may be matrices, as in
Surv objects) and creates one or more summary
statistics that will be computed while the resulting data frame is being
collapsed to one row per condition. The plot method in this case
plots a multi-panel dot chart using the lattice
dotplot function if panel is not specified
to plot. There is an option to print
selected statistics as text on the panels. summaryS pays special
attention to Hmisc variable annotations: label, units.
When panel is specified in addition to fun, a special
x-y plot is made that assumes that the x-axis variable
(typically time) is discrete. This is used for example to plot multiple
quantile intervals as vertical lines next to the main point. A special
panel function mvarclPanel is provided for this purpose.
The plotp method produces corresponding plotly graphics.
When fun is given and panel is omitted, and the result of
fun is a vector of more than one
statistic, the first statistic is taken as the main one. Any columns
with names not in textonly will figure into the calculation of
axis limits. Those in textonly will be printed right under the
dot lines in the dot chart. Statistics with names in textplot
will figure into limits, be plotted, and printed. pch.stats can
be used to specify symbols for statistics after the first column. When
fun computed three columns that are plotted, columns two and
three are taken as confidence limits for which horizontal "error bars"
are drawn. Two levels with different thicknesses are drawn if there are
four plotted summary statistics beyond the first.
mbarclPanel is used to draw multiple vertical lines around the
main points, such as a series of quantile intervals stratified by
x and paneling variables. If mbarclPanel finds a column
of an arument yother that is named "se", and if there are
exactly two levels to a superpositioning variable, the half-height of
the approximate 0.95 confidence interval for the difference between two
point estimates is shown, positioned at the midpoint of the two point
estimates at an x value. This assume normality of point
estimates, and the standard error of the difference is the square root
of the sum of squares of the two standard errors. By positioning the
intervals in this fashion, a failure of the two point estimates to touch
the half-confidence interval is consistent with rejecting the null
hypothesis of no difference at the 0.05 level.
mbarclpl is the sfun function corresponding to
mbarclPanel for plotp, and medvpl is the
sfun replacement for medvPanel.
medvPanel takes raw data and plots median y vs. x,
along with confidence intervals and half-interval for the difference in
medians as with mbarclPanel. Quantile intervals are optional.
Very transparent vertical violin plots are added by default. Unlike
panel.violin, only half of the violin is plotted, and when there
are two superpose groups they are side-by-side in different colors.
For plotp, the function corresponding to medvPanel is
medvpl, which draws back-to-back spike histograms, optional Gini
mean difference, optional SD, quantiles (thin line version of box
plot with 0.05 0.25 0.5 0.75 0.95 quantiles), and half-width confidence
interval for differences in medians. For quantiles, the Harrell-Davis
estimator is used.
summaryS(formula, fun = NULL, data = NULL, subset = NULL,
na.action = na.retain, continuous=10, ...)# S3 method for summaryS
plot(x, formula=NULL, groups=NULL, panel=NULL,
paneldoesgroups=FALSE, datadensity=NULL, ylab='',
funlabel=NULL, textonly='n', textplot=NULL,
digits=3, custom=NULL,
xlim=NULL, ylim=NULL, cex.strip=1, cex.values=0.5, pch.stats=NULL,
key=list(columns=length(groupslevels),
x=.75, y=-.04, cex=.9,
col=trellis.par.get('superpose.symbol')$col, corner=c(0,1)),
outerlabels=TRUE, autoarrange=TRUE, scat1d.opts=NULL, ...)
# S3 method for summaryS
plotp(data, formula=NULL, groups=NULL, sfun=NULL,
fitter=NULL, showpts=! length(fitter), funlabel=NULL,
digits=5, xlim=NULL, ylim=NULL,
shareX=TRUE, shareY=FALSE, autoarrange=TRUE, ...)
mbarclPanel(x, y, subscripts, groups=NULL, yother, ...)
medvPanel(x, y, subscripts, groups=NULL, violin=TRUE, quantiles=FALSE, ...)
mbarclpl(x, y, groups=NULL, yother, yvar=NULL, maintracename='y',
xlim=NULL, ylim=NULL, xname='x', alphaSegments=0.45, ...)
medvpl(x, y, groups=NULL, yvar=NULL, maintracename='y',
xlim=NULL, ylim=NULL, xlab=xname, ylab=NULL, xname='x',
zeroline=FALSE, yother=NULL, alphaSegments=0.45,
dhistboxp.opts=NULL, ...)
a data frame with added attributes for summaryS or a
lattice object ready to render for plot
a formula with possibly multiple left and right-side
variables separated by +. Analysis (response) variables are
on the left and are typically numeric. For plot,
formula is optional and overrides the default formula
inferred for the reshaped data frame.
an optional summarization function, e.g., smean.sd
optional input data frame. For plotp is the object
produced by summaryS.
optional subsetting criteria
function for dealing with NAs when
constructing the model data frame
minimum number of unique values for a numeric variable to have to be considered continuous
ignored for summaryS and mbarclPanel,
passed to strip and panel for plot. Passed to
the density function by medvPanel. For
plotp, are passed to plotlyM and sfun. For
mbarclpl, passed to plotlyM.
an object created by summaryS. For mbarclPanel
is an x-axis argument provided by lattice
a character string or factor specifying that one of the conditioning variables is used for superpositioning and not paneling
optional lattice panel function
set to TRUE if, like
panel.plsmo, the paneling function internally
handles superpositioning for groups
set to TRUE to add rug plots etc. using
scat1d
optional y-axis label
optional axis label for when fun is given
names of statistics to print and not plot. By
default, any statistic named "n" is only printed.
names of statistics to print and plot
used if any statistics are printed as text (including
plotly hovertext), to specify
the number of significant digits to render
a function that customizes formatting of statistics that are printed as text. This is useful for generating plotmath notation. See the example in the tests directory.
optional x-axis limits
optional y-axis limits
size of strip labels
size of statistics printed as text
symbols to use for statistics (not included the one
one in columne one) that are plotted. This is a named
vectors, with names exactly matching those created by
fun. When a column does not have an entry in
pch.stats, no point is drawn for that column.
lattice key specification
set to FALSE to not pass two-way charts
through useOuterStrips
set to FALSE to prevent plot from
trying to optimize which conditioning variable is vertical
a list of options to specify to scat1d
provided by lattice
passed to the panel function from the plot method
based on multiple statistics computed
controls whether violin plots are included
controls whether quantile intervals are included
a function called by plotp.summaryS to compute and
plot user-specified summary measures. Two functions for doing
this are provided here: mbarclpl, medvpl.
a fitting function such as loess to smooth
points. The smoothed values over a systematic grid will be
evaluated and plotted as curves.
set to TRUE to show raw data points in additon
to smoothed curves
TRUE to cause plotly to share a single
x-axis when graphs are aligned vertically
TRUE to cause plotly to share a single
y-axis when graphs are aligned horizontally
a character or factor variable used to stratify the analysis into multiple y-variables
a default trace name when it can't be inferred
x-axis variable name for hover text when it can't be inferred
x-axis label when it can't be inferred
alpha saturation to draw line segments for
plotly
list of options to pass to dhistboxp
set to FALSE to suppress plotly zero
line at x=0
Frank Harrell
summary, summarize
# See tests directory file summaryS.r for more examples, and summarySp.r
# for plotp examples
n <- 100
set.seed(1)
d <- data.frame(sbp=rnorm(n, 120, 10),
dbp=rnorm(n, 80, 10),
age=rnorm(n, 50, 10),
days=sample(1:n, n, TRUE),
S1=Surv(2*runif(n)), S2=Surv(runif(n)),
race=sample(c('Asian', 'Black/AA', 'White'), n, TRUE),
sex=sample(c('Female', 'Male'), n, TRUE),
treat=sample(c('A', 'B'), n, TRUE),
region=sample(c('North America','Europe'), n, TRUE),
meda=sample(0:1, n, TRUE), medb=sample(0:1, n, TRUE))
d <- upData(d, labels=c(sbp='Systolic BP', dbp='Diastolic BP',
race='Race', sex='Sex', treat='Treatment',
days='Time Since Randomization',
S1='Hospitalization', S2='Re-Operation',
meda='Medication A', medb='Medication B'),
units=c(sbp='mmHg', dbp='mmHg', age='Year', days='Days'))
s <- summaryS(age + sbp + dbp ~ days + region + treat, data=d)
# plot(s) # 3 pages
plot(s, groups='treat', datadensity=TRUE,
scat1d.opts=list(lwd=.5, nhistSpike=0))
plot(s, groups='treat', panel=panel.loess, key=list(space='bottom', columns=2),
datadensity=TRUE, scat1d.opts=list(lwd=.5))
# To make a plotly graph when the stratification variable region is not
# present, run the following (showpts adds raw data points):
# plotp(s, groups='treat', fitter=loess, showpts=TRUE)
# Make your own plot using data frame created by summaryP
# xyplot(y ~ days | yvar * region, groups=treat, data=s,
# scales=list(y='free', rot=0))
# Use loess to estimate the probability of two different types of events as
# a function of time
s <- summaryS(meda + medb ~ days + treat + region, data=d)
pan <- function(...)
panel.plsmo(..., type='l', label.curves=max(which.packet()) == 1,
datadensity=TRUE)
plot(s, groups='treat', panel=pan, paneldoesgroups=TRUE,
scat1d.opts=list(lwd=.7), cex.strip=.8)
# Repeat using intervals instead of nonparametric smoother
pan <- function(...) # really need mobs > 96 to est. proportion
panel.plsmo(..., type='l', label.curves=max(which.packet()) == 1,
method='intervals', mobs=5)
plot(s, groups='treat', panel=pan, paneldoesgroups=TRUE, xlim=c(0, 150))
# Demonstrate dot charts of summary statistics
s <- summaryS(age + sbp + dbp ~ region + treat, data=d, fun=mean)
plot(s)
plot(s, groups='treat', funlabel=expression(bar(X)))
# Compute parametric confidence limits for mean, and include sample
# sizes by naming a column "n"
f <- function(x) {
x <- x[! is.na(x)]
c(smean.cl.normal(x, na.rm=FALSE), n=length(x))
}
s <- summaryS(age + sbp + dbp ~ region + treat, data=d, fun=f)
plot(s, funlabel=expression(bar(X) %+-% t[0.975] %*% s))
plot(s, groups='treat', cex.values=.65,
key=list(space='bottom', columns=2,
text=c('Treatment A:','Treatment B:')))
# For discrete time, plot Harrell-Davis quantiles of y variables across
# time using different line characteristics to distinguish quantiles
d <- upData(d, days=round(days / 30) * 30)
g <- function(y) {
probs <- c(0.05, 0.125, 0.25, 0.375)
probs <- sort(c(probs, 1 - probs))
y <- y[! is.na(y)]
w <- hdquantile(y, probs)
m <- hdquantile(y, 0.5, se=TRUE)
se <- as.numeric(attr(m, 'se'))
c(Median=as.numeric(m), w, se=se, n=length(y))
}
s <- summaryS(sbp + dbp ~ days + region, fun=g, data=d)
plot(s, panel=mbarclPanel)
plot(s, groups='region', panel=mbarclPanel, paneldoesgroups=TRUE)
# For discrete time, plot median y vs x along with CL for difference,
# using Harrell-Davis median estimator and its s.e., and use violin
# plots
s <- summaryS(sbp + dbp ~ days + region, data=d)
plot(s, groups='region', panel=medvPanel, paneldoesgroups=TRUE)
# Proportions and Wilson confidence limits, plus approx. Gaussian
# based half/width confidence limits for difference in probabilities
g <- function(y) {
y <- y[!is.na(y)]
n <- length(y)
p <- mean(y)
se <- sqrt(p * (1. - p) / n)
structure(c(binconf(sum(y), n), se=se, n=n),
names=c('Proportion', 'Lower', 'Upper', 'se', 'n'))
}
s <- summaryS(meda + medb ~ days + region, fun=g, data=d)
plot(s, groups='region', panel=mbarclPanel, paneldoesgroups=TRUE)
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