# R/ecdf.ks.CI.R In NSM3: Functions and Datasets to Accompany Hollander, Wolfe, and Chicken - Nonparametric Statistical Methods, Third Edition

#### Documented in ecdf.ks.CI

```ecdf.ks.CI<-function(x, main = NULL, sub = NULL,
xlab = deparse(substitute(x)), ...)
{
# Adapted from code by Kjetil Halvorsen found here:
#https://stat.ethz.ch/pipermail/r-help/2003-July/036643.html

approx.ksD = function(n)
{
## approximations for the critical level for Kolmogorov-Smirnov
## statistic D,
## for confidence level 0.95. Taken from Bickel & Doksum, table IX,
## p.483
## and Lienert G.A.(1975) who attributes to Miller,L.H.(1956), JASA
ifelse(n > 80,
1.358 /( sqrt(n) + .12 + .11/sqrt(n)),##Bickel&Doksum, table
##IX,p.483

splinefun(c(1:9, 10, 15, 10 * 2:8),# from Lienert
c(.975,   .84189, .70760, .62394, .56328,# 1:5
.51926, .48342, .45427, .43001, .40925,# 6:10
.33760, .29408, .24170, .21012,# 15,20,30,40
.18841, .17231, .15975, .14960)) (n))
}
xlab
if(is.null(main))
main <- paste("ecdf(",deparse(substitute(x)),") + 95% K.S.bands",
sep="")
n <- length(x)
if(is.null(sub))
sub <- paste("n = ", n)
ec <- ecdf(x)
xx <- get("x", envir=environment(ec))# = sort(x)
yy <- get("y", envir=environment(ec))
D <- approx.ksD(n)
yyu <- pmin(yy+D, 1)
yyl <- pmax(yy-D, 0)
ecu <- stepfun(xx, c(yyu, 1) )
ecl <- stepfun(xx, c(yyl, yyl[n]) )

## Plots -- all calling  plot.stepfun

plot(ec, main = main, sub = sub, xlab = xlab, ...)