Performs one or two sample Kolmogorov-Smirnov tests.
ks.test(x, y, ..., alternative = c("two.sided", "less", "greater"), exact = NULL, tol=1e-8, simulate.p.value=FALSE, B=2000)
- a numeric vector of data values.
- a numeric vector of data values, or a character string naming a cumulative distribution function or an actual cumulative distribution function such as
ycan be an
ecdffunction (or an object of class
stepfun) for specifying a discrete distribution.
- parameters of the distribution specified (as a character string) by
- indicates the alternative hypothesis and must be one of
"greater". You can specify just the initial letter of the value, but the argument name must be give in full. See ‘Details’ for the meanings of the possible values.
NULLor a logical indicating whether an exact p-value should be computed. See ‘Details’ for the meaning of
NULL. Not used for the one-sided two-sample case.
- used as an upper bound for possible rounding error in values (say,
b) when needing to check for equality (
a==b); value of
- a logical indicating whether to compute p-values by Monte Carlo simulation, for discrete goodness-of-fit tests only.
- an integer specifying the number of replicates used in the Monte Carlo test (for discrete goodness-of-fit tests only).
y is numeric, a two-sample test of the null hypothesis that
y were drawn from the same continuous distribution is performed.
y can be a character string naming a continuous (cumulative) distribution function (or such a function), or an
ecdf function (or object of class
stepfun) giving a discrete distribution. In these cases, a one-sample test is carried out of the null that the distribution function which generated
x is distribution
y with parameters specified by
The presence of ties generates a warning unless
y describes a discrete distribution (see above), since continuous distributions do not generate them.
The possible values
alternative specify the null hypothesis that the true distribution function of
x is equal to, not less than or not greater than the hypothesized distribution function (one-sample case) or the distribution function of
y (two-sample case), respectively. This is a comparison of cumulative distribution functions, and the test statistic is the maximum difference in value, with the statistic in the
"greater" alternative being D^+ = max[F_x(u) - F_y(u)]. Thus in the two-sample case
alternative="greater" includes distributions for which
x is stochastically smaller than
y (the CDF of
x lies above and hence to the left of that for
y), in contrast to
Exact p-values are not available for the one-sided two-sample case, or in the case of ties if
y is continuous. If
exact = NULL (the default), an exact p-value is computed if the sample size is less than 100 in the one-sample case with
y continuous or if the sample size is less than or equal to 30 with
y discrete; or if the product of the sample sizes is less than 10000 in the two-sample case for continuous
y. Otherwise, asymptotic distributions are used whose approximations may be inaccurate in small samples. With
y continuous, the one-sample two-sided case, exact p-values are obtained as described in Marsaglia, Tsang & Wang (2003); the formula of Birnbaum & Tingey (1951) is used for the one-sample one-sided case.
In the one-sample case with
y discrete, the methods presented in Conover (1972) and Gleser (1985) are used when
exact=TRUE (or when
length(x)<=30 as described above. When
length(x)>30, the test is not exact and the resulting p-values are known to be conservative. Usage of
exact=TRUE with sample sizes greater than 30 is not advised due to numerical instabilities; in such cases, simulated p-values may be desirable.
If a single-sample test is used with
y continuous, the parameters specified in
... must be pre-specified and not estimated from the data. There is some more refined distribution theory for the KS test with estimated parameters (see Durbin, 1973), but that is not implemented in
A list with class
"htest" containing the following components:
- the value of the test statistic.
- the p-value of the test.
- a character string describing the alternative hypothesis.
- a character string indicating what type of test was performed.
- a character string giving the name(s) of the data.
Z. W. Birnbaum and Fred H. Tingey (1951), One-sided confidence contours for probability distribution functions. The Annals of Mathematical Statistics, 22/4, 592--596.
William J. Conover (1971), Practical Nonparametric Statistics. New York: John Wiley & Sons. Pages 295--301 (one-sample Kolmogorov test), 309--314 (two-sample Smirnov test).
William J. Conover (1972), A Kolmogorov Goodness-of-Fit Test for Discontinuous Distributions. Journal of American Statistical Association, Vol. 67, No. 339, 591--596.
Leon Jay Gleser (1985), Exact Power of Goodness-of-Fit Tests of Kolmogorov Type for Discontinuous Distributions. Journal of American Statistical Association, Vol. 80, No. 392, 954--958.
Durbin, J. (1973) Distribution theory for tests based on the sample distribution function. SIAM.
George Marsaglia, Wai Wan Tsang and Jingbo Wang (2003), Evaluating Kolmogorov's distribution. Journal of Statistical Software, 8/18. http://www.jstatsoft.org/v08/i18/.
shapiro.test which performs the Shapiro-Wilk test for normality;
cvm.test for Cramer-von Mises type tests.
require(graphics) require(dgof) set.seed(1) x <- rnorm(50) y <- runif(30) # Do x and y come from the same distribution? ks.test(x, y) # Does x come from a shifted gamma distribution with shape 3 and rate 2? ks.test(x+2, "pgamma", 3, 2) # two-sided, exact ks.test(x+2, "pgamma", 3, 2, exact = FALSE) ks.test(x+2, "pgamma", 3, 2, alternative = "gr") # test if x is stochastically larger than x2 x2 <- rnorm(50, -1) plot(ecdf(x), xlim=range(c(x, x2))) plot(ecdf(x2), add=TRUE, lty="dashed") t.test(x, x2, alternative="g") wilcox.test(x, x2, alternative="g") ks.test(x, x2, alternative="l") ######################################################### # TBA, JWE new examples added for discrete distributions: x3 <- sample(1:10, 25, replace=TRUE) # Using ecdf() to specify a discrete distribution: ks.test(x3, ecdf(1:10)) # Using step() to specify the same discrete distribution: myfun <- stepfun(1:10, cumsum(c(0, rep(0.1, 10)))) ks.test(x3, myfun) # The previous R ks.test() does not correctly calculate the # test statistic for discrete distributions (gives warning): # stats::ks.test(c(0, 1), ecdf(c(0, 1))) # ks.test(c(0, 1), ecdf(c(0, 1))) # Even when the correct test statistic is given, the # previous R ks.test() gives conservative p-values: stats::ks.test(rep(1, 3), ecdf(1:3)) ks.test(rep(1, 3), ecdf(1:3)) ks.test(rep(1, 3), ecdf(1:3), simulate=TRUE, B=10000)
Documentation reproduced from package dgof, version 1.2. License: GPL (>= 2.0)