summaryRc
From Hmisc v4.02
by Frank E Harrell Jr
Graphical Summarization of Continuous Variables Against a Response
summaryRc
is a continuous version of summary.formula
with method='response'
. It uses the plsmo
function to compute the possibly stratified lowess
nonparametric regression estimates, and plots them along with the data
density, with selected quantiles of the overall distribution (over
strata) of each x
shown as arrows on top of the graph. All the
x
variables must be numeric and continuous or nearly continuous.
 Keywords
 hplot
Usage
summaryRc(formula, data=NULL, subset=NULL, na.action=NULL, fun = function(x) x, na.rm = TRUE, ylab=NULL, ylim=NULL, xlim=NULL, nloc=NULL, datadensity=NULL, quant = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.90, 0.95), quantloc=c('top','bottom'), cex.quant=.6, srt.quant=0, bpplot = c('none', 'top', 'top outside', 'top inside', 'bottom'), height.bpplot=0.08, trim=NULL, test = FALSE, vnames = c('labels', 'names'), ...)
Arguments
 formula

An R formula with additive effects. The
formula
may contain one or more invocations of thestratify
function whose arguments are defined below. This causes the entire analysis to be stratified by crossclassifications of the combined list of stratification factors. This stratification will be reflected as separatelowess
curves.  data
 name or number of a data frame. Default is the current frame.
 subset
 a logical vector or integer vector of subscripts used to specify the subset of data to use in the analysis. The default is to use all observations in the data frame.
 na.action

function for handling missing data in the input data. The default is
a function defined here called
na.retain
, which keeps all observations for processing, with missing variables or not.  fun

function for transforming
lowess
estimates. Default is the identity function.  na.rm

TRUE
(the default) to excludeNA
s before passing data tofun
to compute statistics,FALSE
otherwise.  ylab
y
axis label. Default is label attribute ofy
variable, or its name. ylim
y
axis limits. By default each graph is scaled on its own. xlim
 a list with elements named as the variable names appearing
on the
x
axis, with each element being a 2vector specifying lower and upper limits. Any variable not appearing in the list will have its limits computed and possiblytrim
med.  nloc
 location for sample size. Specify
nloc=FALSE
to suppress, ornloc=list(x=,y=)
wherex,y
are relative coordinates in the data window. Default position is in the largest empty space.  datadensity
 see
plsmo
. Defaults toTRUE
if there is astratify
variable,FALSE
otherwise.  quant

vector of quantiles to use for summarizing the marginal distribution
of each
x
. This must be numbers between 0 and 1 inclusive. UseNULL
to omit quantiles.  quantloc
 specify
quantloc='bottom'
to place at the bottom of each plot rather than the default  cex.quant
 character size for writing which quantiles are
represented. Set to
0
to suppress quantile labels.  srt.quant
 angle for text for quantile labels
 bpplot
 if not
'none'
will draw extended box plot at location given bybpplot
, and quantiles discussed above will be suppressed. Specifyingbpplot='top'
is the same as specifyingbpplot='top inside'
.  height.bpplot
 height in inches of the horizontal extended box plot
 trim
 The default is to plot from the 10th smallest to the 10th
largest
x
if the number of nonNAs exceeds 200, otherwise to use the entire range ofx
. Specify another quantile to use other limits, e.g.,trim=0.01
will use the first and last percentiles  test

Set to
TRUE
to plot test statistics (not yet implemented).  vnames

By default, plots are usually labeled with variable labels
(see the
label
andsas.get
functions). To use the shorter variable names, specifyvnames="names"
.  ...
 arguments passed to
plsmo
Value
See Also
Examples
options(digits=3)
set.seed(177)
sex < factor(sample(c("m","f"), 500, rep=TRUE))
age < rnorm(500, 50, 5)
bp < rnorm(500, 120, 7)
units(age) < 'Years'; units(bp) < 'mmHg'
label(bp) < 'Systolic Blood Pressure'
L < .5*(sex == 'm') + 0.1 * (age  50)
y < rbinom(500, 1, plogis(L))
par(mfrow=c(1,2))
summaryRc(y ~ age + bp)
# For x limits use 1st and 99th percentiles to frame extended box plots
summaryRc(y ~ age + bp, bpplot='top', datadensity=FALSE, trim=.01)
summaryRc(y ~ age + bp + stratify(sex),
label.curves=list(keys='lines'), nloc=list(x=.1, y=.05))
y2 < rbinom(500, 1, plogis(L + .5))
Y < cbind(y, y2)
summaryRc(Y ~ age + bp + stratify(sex),
label.curves=list(keys='lines'), nloc=list(x=.1, y=.05))
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