labcurve function is used (by default) to label
the multiple step functions or to draw a legend defining line types, colors,
or symbols by linking them with group labels. A
weights vector may
be specified to get weighted estimates. Specify
normwt to make
weights
sum to the length of
x (after removing NAs). Other wise
the total sample size is taken to be the sum of the weights.
ecdf is actually a method, and
ecdf.default is what's
called for a vector argument.
ecdf.data.frame is called when the
first argument is a data frame. This function can automatically set up
a matrix of ECDFs and wait for a mouse click if the matrix requires more
than one page. Categorical variables, character variables, and
variables having fewer than a set number of unique values are ignored.
If
par(mfrow=..) is not set up before
ecdf.data.frame is
called, the function will try to figure the best layout depending on the
number of variables in the data frame. Upon return the original
mfrow
is left intact.
When the first argument to
ecdf is a formula, a Trellis/Lattice function
ecdf.formula
is called. This allows for multi-panel
conditioning, superposition using a
groups variable, and other
Trellis features, along with the ability to easily plot transformed
ECDFs using the
fun argument. For example, if
fun=qnorm,
the inverse normal transformation will be used for the y-axis. If the
transformed curves are linear this indicates normality. Like the
xYplot
function,
ecdf will create a function
Key if
the
groups variable is used. This function can be invoked by the
user to define the keys for the groups.
ecdf(x, ...)
## Default S3 method:
ecdf(x, what=c('F','1-F','f'), weights, normwt=FALSE,
xlab, ylab, q, pl=TRUE, add=FALSE, lty=1,
col=1, group=rep(1,length(x)), label.curves=TRUE, xlim,
subtitles=TRUE, datadensity=c('none','rug','hist','density'),
side=1,
frac=switch(datadensity,none=NA,rug=.03,hist=.1,density=.1),
dens.opts=NULL, lwd, ...)
## S3 method for class 'data.frame':
ecdf(x, group=rep(1,nrows), weights, normwt,
label.curves=TRUE, n.unique=10, na.big=FALSE, subtitles=TRUE,
vnames=c('labels','names'),...)
## S3 method for class 'formula':
ecdf(x, data, groups, prepanel=prepanel.ecdf,
panel=panel.ecdf, ..., xlab, ylab, fun=function(x)x, subset=TRUE)
"F" which results in plotting the fraction of values
<= x. Set to
"1-F" to plot the fraction > x or
"f" to plot the
cumulative frequency of values <= x.
ecdf.formula,
xlab defaults to the
label attribute
of the x-axis variable.
"Proportion <= x",
"Proportion > x",
or "Frequency <= x" depending on value of
what.
group is specified, this can be a vector.
groups.
factor categorical variable used for stratifying
estimates. If
group is present, as many ECDFs are drawn as there are
non–missing group levels.
group exists.
Default is
TRUE to use
labcurve to label curves where they are farthest
apart. Set
label.curves to a
list to specify options to
labcurve, e.g.,
label.curves=list(method="arrow", cex=.8).
These option names may be abbreviated in the usual way arguments
are abbreviated. Use for example
label.curves=list(keys=1:5)
to draw symbols periodically (as in
pch=1:5 - see
points)
on the curves and automatically position a legend
in the most empty part of the plot. Set
label.curves=FALSE to
suppress drawing curve labels. The
col,
lty, and
type
parameters are automatically passed to
labcurve, although you
can override them here. You can set
label.curves=list(keys="lines") to
have different line types defined in an automatically positioned key.
x.
FALSE to suppress putting a subtitle at the bottom left of each
plot. The subtitle indicates the numbers of
non-missing and missing observations, which are labeled
n,
m.
datadensity is not
"none", either
scat1d or
histSpike is called to
add a rug plot (
datadensity="rug"), spike histogram
(
datadensity="hist"), or smooth density estimate (
"density") to
the bottom or top of the ECDF.
datadensity is not
"none", the default is to place the additional
information on top of the x-axis (
side=1). Use
side=3 to place at
the top of the graph.
histSpike
histSpike
ecdf.default.
For
ecdf.formula, if
groups is not used, you can also add
data density information to each panel's ECDF by specifying the
datadensity and optional
frac,
side,
dens.opts arguments.
TRUE to draw the number of NAs in larger letters in the middle of
the plot for
ecdf.data.frame
vnames="names"
to instead use variable names.
wtd.ecdf. The default is to use the standard
"i/n" method as is
used by the non-Trellis versions of
ecdf.
ecdf
groups
causing
ecdf.formula to overlay multiple ECDFs on one panel.
ecdf.default an invisible list with elements x and y giving the
coordinates of the cdf. If there is more than one
group, a list of
such lists is returned. An attribute,
N, is in the returned
object. It contains the elements
n and
m, the number of
non-missing and missing observations, respectively.
Frank Harrell
Department of Biostatistics, Vanderbilt University
mailto:f.harrell@vanderbilt.edu
set.seed(1)
ch <- rnorm(1000, 200, 40)
ecdf(ch, xlab="Serum Cholesterol")
scat1d(ch) # add rug plot
histSpike(ch, add=TRUE, frac=.15) # add spike histogram
# Better: add a data density display automatically:
ecdf(ch, datadensity='density')
label(ch) <- "Serum Cholesterol"
ecdf(ch)
other.ch <- rnorm(500, 220, 20)
ecdf(other.ch,add=TRUE,lty=2)
sex <- factor(sample(c('female','male'), 1000, TRUE))
ecdf(ch, q=c(.25,.5,.75)) # show quartiles
ecdf(ch, group=sex,
label.curves=list(method='arrow'))
# Example showing how to draw multiple ECDFs from paired data
pre.test <- rnorm(100,50,10)
post.test <- rnorm(100,55,10)
x <- c(pre.test, post.test)
g <- c(rep('Pre',length(pre.test)),rep('Post',length(post.test)))
ecdf(x, group=g, xlab='Test Results', label.curves=list(keys=1:2))
# keys=1:2 causes symbols to be drawn periodically on top of curves
# Draw a matrix of ECDFs for a data frame
m <- data.frame(pre.test, post.test,
sex=sample(c('male','female'),100,TRUE))
ecdf(m, group=m$sex, datadensity='rug')
freqs <- sample(1:10, 1000, TRUE)
ecdf(ch, weights=freqs) # weighted estimates
# Trellis/Lattice examples:
region <- factor(sample(c('Europe','USA','Australia'),100,TRUE))
year <- factor(sample(2001:2002,1000,TRUE))
ecdf(~ch | region*year, groups=sex)
Key() # draw a key for sex at the default location
# Key(locator(1)) # user-specified positioning of key
age <- rnorm(1000, 50, 10)
ecdf(~ch | equal.count(age), groups=sex) # use overlapping shingles
ecdf(~ch | sex, datadensity='hist', side=3) # add spike histogram at top