Plot the ECDF of a uniform sample with Kolmogorov-Smirnov bounds

This plot function takes a univariate sample that should be tested for a U(0,1) distribution, plots its empirical cumulative distribution function (ecdf), and adds a confidence band by inverting the corresponding Kolmogorov-Smirnov test (ks.test). The uniform distribution is rejected if the ECDF is not completely inside the confidence band.

Usage

ks.plot.unif(U, conf.level = 0.95, exact = NULL,
             col.conf = "gray", col.ref = "gray",
             xlab = expression(u[(i)]), ylab = "Cumulative distribution")

Arguments

U: numeric vector containing the sample. Missing values are (silently) ignored.
conf.level: confidence level for the K-S-test (defaults to 0.95), can also be a vector of multiple levels.
exact: see ks.test.
col.conf: colour of the confidence lines.
col.ref: colour of the diagonal reference line.
xlab, ylab: axis labels.

Value

NULL (invisibly).

Author

Michael Höhle and Sebastian Meyer.

The code re-uses fragments from the ks.test source file https://svn.R-project.org/R/trunk/src/library/stats/R/ks.test.R, with Copyright (C) 1995-2022 The R Core Team, available under GPL-2 (or later), and C functionality from the source file https://svn.R-project.org/R/trunk/src/library/stats/src/ks.c, partially based on code published in Marsaglia et al. (2003), with Copyright (C) 1999-2022 The R Core Team, also available under GPL-2 (or later).

References

George Marsaglia and Wai Wan Tsang and Jingbo Wang (2003): Evaluating Kolmogorov's distribution. Journal of Statistical Software, 8 (18). doi:10.18637/jss.v008.i18

Examples

samp <- runif(99)
ks.plot.unif(samp, conf.level=c(0.95, 0.99), exact=TRUE)
ks.plot.unif(samp, conf.level=c(0.95, 0.99), exact=FALSE)