ks.test                package:stats                R Documentation

Kolmogorov-Smirnov Tests

Description:

     Performs one or two sample Kolmogorov-Smirnov tests.

Usage:

     ks.test(x, y, ...,
             alternative = c("two.sided", "less", "greater"),
             exact = NULL)

Arguments:

       x: a numeric vector of data values.

       y: either a numeric vector of data values, or a character string
          naming a distribution function.

     ...: parameters of the distribution specified (as a character
          string) by 'y'.

alternative: indicates the alternative hypothesis and must be one of
          '"two.sided"' (default), '"less"', or '"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.

   exact: 'NULL' or 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.

Details:

     If 'y' is numeric, a two-sample test of the null hypothesis that
     'x' and 'y' were drawn from the same _continuous_ distribution is
     performed.

     Alternatively, 'y' can be a character string naming a continuous
     distribution function.  In this case, 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, since continuous
     distributions do not generate them.

     The possible values '"two.sided"', '"less"' and '"greater"' of
     '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_u [ 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 't.test' or
     'wilcox.test'.

     Exact p-values are not available for the one-sided two-sample
     case, or in the case of ties.  If 'exact = NULL' (the default), an
     exact p-value is computed if the sample size is less than 100 in
     the one-sample case, and if the product of the sample sizes is
     less than 10000 in the two-sample case.  Otherwise, asymptotic
     distributions are used whose approximations may be inaccurate in
     small samples.  In 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.

     If a single-sample test is used, 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 'ks.test'.

Value:

     A list with class '"htest"' containing the following components: 

statistic: the value of the test statistic.

 p.value: the p-value of the test.

alternative: a character string describing the alternative hypothesis.

  method: a character string indicating what type of test was
          performed.

data.name: a character string giving the name(s) of the data.

References:

     Z. W. Birnbaum & 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).

     Durbin, J. (1973) _Distribution theory for tests based on the
     sample distribution function_.  SIAM.

     George Marsaglia, Wai Wan Tsang & Jingbo Wang (2003), Evaluating
     Kolmogorov's distribution. _Journal of Statistical Software_,
     *8*/18. <URL: http://www.jstatsoft.org/v08/i18/>.

See Also:

     'shapiro.test' which performs the Shapiro-Wilk test for normality.

Examples:

     require(graphics)

     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")

