summaryS

0th

Percentile

Summarize Multiple Response Variables and Make Multipanel Scatter or Dot Plot

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.

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.

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.

Keywords
manip, hplot, category, grouping
Usage
summaryS(formula, fun = NULL, data = NULL, subset = NULL, na.action = na.retain, continuous=10, ...)
"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, ...)
mbarclPanel(x, y, subscripts, groups=NULL, yother, ...)
medvPanel(x, y, subscripts, groups=NULL, violin=TRUE, quantiles=FALSE, ...)
Arguments
formula
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.
fun
an optional summarization function, e.g., smean.sd
data
optional input data frame
subset
optional subsetting criteria
na.action
function for dealing with NAs when constructing the model data frame
continuous
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.
x
an object created by summaryS. For mbarclPanel is an x-axis argument provided by lattice
groups
a character string specifying that one of the conditioning variables is used for superpositioning and not paneling
panel
optional lattice panel function
paneldoesgroups
set to TRUE if, like panel.plsmo, the paneling function internally handles superpositioning for groups
datadensity
set to TRUE to add rug plots etc. using scat1d
ylab
optional y-axis label
funlabel
optional axis label for when fun is given
textonly
names of statistics to print and not plot. By default, any statistic named "n" is only printed.
textplot
names of statistics to print and plot
digits
used if any statistics are printed as text, to specify the number of digits to the right of the decimal point to print
custom
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.
xlim
optional x-axis limits
ylim
optional y-axis limits
cex.strip
size of strip labels
cex.values
size of statistics printed as text
pch.stats
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.
key
lattice key specification
outerlabels
set to FALSE to not pass two-way charts through useOuterStrips
autoarrange
set to FALSE to prevent plot from trying to optimize which conditioning variable is vertical
scat1d.opts
a list of options to specify to scat1d
y, subscripts
provided by lattice
yother
passed to the panel function from the plot method based on multiple statistics computed
violin
controls whether violin plots are included
quantiles
controls whether quantile intervals are included
Value

summaryS or a lattice object ready to render for plot

See Also

summary, summarize

Aliases
  • summaryS
  • plot.summaryS
  • mbarclPanel
  • medvPanel
Examples
# See tests directory file summaryS.r for more 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))

# 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)

# 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)
Documentation reproduced from package Hmisc, version 4.0-2, License: GPL (>= 2)

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