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.
ks.plot.unif(U, conf.level = 0.95, exact = NULL,
col.conf = "gray", col.ref = "gray",
xlab = expression(u[(i)]), ylab = "Cumulative distribution")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 |
col.conf |
colour of the confidence lines. |
col.ref |
colour of the diagonal reference line. |
xlab, ylab |
axis labels. |
NULL (invisibly).
Michael Höhle and Sebastian Meyer.
The code contains segments originating from the source of the ks.test function https://svn.R-project.org/R/trunk/src/library/stats/R/ks.test.R, which is Copyright (C) 1995-2012 The R Core Team available under GPL-2 (or later) and C functionality from https://svn.R-project.org/R/trunk/src/library/stats/src/ks.c, which is copyright (C) 1999-2009 the R Core Team and available under GPL-2 (or later). Somewhat hidden in their ‘ks.c’ file is a statement that part of their code is based on code published in Marsaglia et al. (2003).
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
ks.test for the Kolmogorov-Smirnov test, as well as
checkResidualProcess, which makes use of this plot
function.
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)
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