ecdf {stats}  R Documentation 
Compute an empirical cumulative distribution function, with several methods for plotting, printing and computing with such an “ecdf” object.
ecdf(x) ## S3 method for class 'ecdf' plot(x, ..., ylab="Fn(x)", verticals = FALSE, col.01line = "gray70", pch = 19) ## S3 method for class 'ecdf' print(x, digits= getOption("digits")  2, ...) ## S3 method for class 'ecdf' summary(object, ...) ## S3 method for class 'ecdf' quantile(x, ...)
x, object 
numeric vector of the observations for 
... 
arguments to be passed to subsequent methods, e.g.,

ylab 
label for the yaxis. 
verticals 
see 
col.01line 
numeric or character specifying the color of the
horizontal lines at y = 0 and 1, see 
pch 
plotting character. 
digits 
number of significant digits to use, see

The e.c.d.f. (empirical cumulative distribution function) Fn is a step function with jumps i/n at observation values, where i is the number of tied observations at that value. Missing values are ignored.
For observations
x
= (x1,x2, ... xn),
Fn is the fraction of observations less or equal to t,
i.e.,
Fn(t) = #{xi <= t}/n = 1/n sum(i=1,n) Indicator(xi <= t).
The function plot.ecdf
which implements the plot
method for ecdf
objects, is implemented via a call to
plot.stepfun
; see its documentation.
For ecdf
, a function of class "ecdf"
, inheriting from the
"stepfun"
class, and hence inheriting a
knots()
method.
For the summary
method, a summary of the knots of object
with a "header"
attribute.
The quantile(obj, ...)
method computes the same quantiles as
quantile(x, ...)
would where x
is the original sample.
The objects of class "ecdf"
are not intended to be used for
permanent storage and may change structure between versions of R (and
did at R 3.0.0). They can usually be recreated by
eval(attr(old_obj, "call"), environment(old_obj))
since the data used is stored as part of the object's environment.
Martin Maechler; fixes and new features by other Rcore members.
stepfun
, the more general class of step functions,
approxfun
and splinefun
.
## Simple didactical ecdf example : x < rnorm(12) Fn < ecdf(x) Fn # a *function* Fn(x) # returns the percentiles for x tt < seq(2, 2, by = 0.1) 12 * Fn(tt) # Fn is a 'simple' function {with values k/12} summary(Fn) ##> see below for graphics knots(Fn) # the unique data values {12 of them if there were no ties} y < round(rnorm(12), 1); y[3] < y[1] Fn12 < ecdf(y) Fn12 knots(Fn12) # unique values (always less than 12!) summary(Fn12) summary.stepfun(Fn12) ## Advanced: What's inside the function closure? ls(environment(Fn12)) ##[1] "f" "method" "n" "x" "y" "yleft" "yright" utils::ls.str(environment(Fn12)) stopifnot(all.equal(quantile(Fn12), quantile(y))) ### Plotting  require(graphics) op < par(mfrow = c(3, 1), mgp = c(1.5, 0.8, 0), mar = .1+c(3,3,2,1)) F10 < ecdf(rnorm(10)) summary(F10) plot(F10) plot(F10, verticals = TRUE, do.points = FALSE) plot(Fn12 , lwd = 2) ; mtext("lwd = 2", adj = 1) xx < unique(sort(c(seq(3, 2, length = 201), knots(Fn12)))) lines(xx, Fn12(xx), col = "blue") abline(v = knots(Fn12), lty = 2, col = "gray70") plot(xx, Fn12(xx), type = "o", cex = .1) # plot.default {ugly} plot(Fn12, col.hor = "red", add = TRUE) # plot method abline(v = knots(Fn12), lty = 2, col = "gray70") ## luxury plot plot(Fn12, verticals = TRUE, col.points = "blue", col.hor = "red", col.vert = "bisque") ## this works too (automatic call to ecdf(.)): plot.ecdf(rnorm(24)) title("via simple plot.ecdf(x)", adj = 1) par(op)