# summaryS

##### 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.

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.

##### Usage

```
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, …)

##### 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. For

`plotp`

is the object produced by`summaryS`

.- subset
optional subsetting criteria

- na.action
function for dealing with

`NA`

s 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`

. For`plotp`

, are passed to`plotlyM`

and`sfun`

. For`mbarclpl`

, passed to`plotlyM`

.- x
an object created by

`summaryS`

. For`mbarclPanel`

is an`x`

-axis argument provided by`lattice`

- groups
a character string or factor 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 (including

`plotly`

hovertext), to specify the number of significant digits to render- 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

- sfun
a function called by

`plotp.summaryS`

to compute and plot user-specified summary measures. Two functions for doing this are provided here:`mbarclpl, medvpl`

.- fitter
a fitting function such as

`loess`

to smooth points. The smoothed values over a systematic grid will be evaluated and plotted as curves.- showpts
set to

`TRUE`

to show raw data points in additon to smoothed curves- shareX
`TRUE`

to cause`plotly`

to share a single x-axis when graphs are aligned vertically- shareY
`TRUE`

to cause`plotly`

to share a single y-axis when graphs are aligned horizontally- yvar
a character or factor variable used to stratify the analysis into multiple y-variables

- maintracename
a default trace name when it can't be inferred

- xname
x-axis variable name for hover text when it can't be inferred

- xlab
x-axis label when it can't be inferred

- alphaSegments
alpha saturation to draw line segments for

`plotly`

- dhistboxp.opts
`list`

of options to pass to`dhistboxp`

- zeroline
set to

`FALSE`

to suppress`plotly`

zero line at x=0

##### Value

a data frame with added attributes for `summaryS`

or a
`lattice`

object ready to render for `plot`

##### See Also

##### Examples

```
# NOT RUN {
# 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))
# 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)
# }
```

*Documentation reproduced from package Hmisc, version 4.3-1, License: GPL (>= 2)*