ks.test {ctest} | R Documentation |
Performs one or two sample Kolmogorov-Smirnov tests.
ks.test(x, y, ..., alternative = c("two.sided", "less", "greater"),
exact = NULL)
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 by |
alternative |
indicates the alternative hypothesis and must be
one of |
exact |
|
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 of the null
that the distribution function underlying x
is y
with
parameters specified by ...
is carried out.
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.
Exact p-values are only available for the two-sided two-sample test
with no ties. In that case, if exact = NULL
(the default) an
exact p-value is computed if the product of the sample sizes is less
than 10000. Otherwise, asymptotic distributions are used whose
approximations may be inaccurate in small samples.
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. |
Conover, W. J. (1971), Practical nonparametric statistics. New York: John Wiley & Sons. Pages 295–301 (one-sample “Kolmogorov” test), 309–314 (two-sample “Smirnov” test).
shapiro.test
which performs the Shapiro-Wilk test for
normality.
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 scale 2?
ks.test(x+2, "pgamma", 3, 2) # two-sided
ks.test(x+2, "pgamma", 3, 2, alternative = "gr")