ecdf(x) plot(ecdf(x), verticals = FALSE, col.01line = "gray70", ...)
x
| numeric vector with the ``observations''. |
x, ecdf(x) returns the
e.c.d.f. (empirical cumulative distribution function), i.e.
the function Fn. Given the observations,
x= (x1,x2,...xn),
Fn is the fraction of observations less or equal to t,
i.e.,
Fn(t) = #{x_i <= 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.
"ecdf", inheriting from the
"stepfun" class.stepfun, the more general class of step functions,
approxfun and splinefun.##-- Simple didactical ecdf example: Fn <- ecdf(rnorm(12)) Fn; summary(Fn) 12*Fn(knots(Fn)) == 1:12 ## == 1:12 iff there are no ties ! y <- round(rnorm(12),1); y[3] <- y[1] Fn12 <- ecdf(y) Fn12 print(knots(Fn12), dig=2) 12*Fn12(knots(Fn12)) ## ~= 1:12 if there where no ties summary(Fn12) summary.stepfun(Fn12) print(ls.Fn12 <- ls(env= environment(Fn12))) ##[1] "f" "method" "n" "x" "y" "yleft" "yright" 12 * Fn12((-20:20)/10) ###----------------- Plotting -------------------------- 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.p = F) plot(Fn12)# , lwd=2) dis-regarded 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='b', cex=.1)#- plot.default plot(Fn12, col.h='red', add= TRUE) #- plot method abline(v=knots(Fn12),lty=2,col='gray70') plot(Fn12, verticals=T, col.p='blue', col.h='red',col.v='bisque') par(op) ##-- this works too (automatic call to ecdf(.)): plot.ecdf(rnorm(24))