ks.plot.unif | R Documentation |
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 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).
George Marsaglia and Wai Wan Tsang and Jingbo Wang (2003): Evaluating Kolmogorov's distribution. Journal of Statistical Software, 8 (18). \Sexpr[results=rd]{tools:::Rd_expr_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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