\Header{wilcox.test}{Wilcoxon Rank Sum and Signed Rank Tests}
\methalias{wilcox.test.default}{wilcox.test}
\methalias{wilcox.test.formula}{wilcox.test}
\keyword{htest}{wilcox.test}
\begin{Description}\relax
Performs one and two sample Wilcoxon tests on vectors of data.\end{Description}
\begin{Usage}
\begin{verbatim}
wilcox.test(x, y = NULL, alternative = c("two.sided", "less", "greater"),
            mu = 0, paired = FALSE, exact = NULL, correct = TRUE, 
            conf.int = FALSE, conf.level = 0.95, ...)
wilcox.test(formula, data, subset, na.action, ...)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{x}] numeric vector of data values.
\item[\code{y}] an optional numeric vector of data values.
\item[\code{alternative}] a character string specifying the alternative
hypothesis, must be one of \code{"two.sided"} (default),
\code{"greater"} or \code{"less"}.  You can specify just the initial
letter.
\item[\code{mu}] a number specifying an optional location parameter.
\item[\code{paired}] a logical indicating whether you want a paired test.
\item[\code{exact}] a logical indicating whether an exact p-value should be
computed.
\item[\code{correct}] a logical indicating whether to apply continuity
correction in the normal approximation for the p-value.
\item[\code{conf.int}] a logical indicating whether a confidence interval
should be computed.
\item[\code{conf.level}] confidence level of the interval.
\item[\code{formula}] a formula of the form \code{lhs \textasciitilde{} rhs} where \code{lhs}
is a numeric variable giving the data values and \code{rhs} a factor
with two levels giving the corresponding groups.
\item[\code{data}] an optional data frame containing the variables in the
model formula.
\item[\code{subset}] an optional vector specifying a subset of observations
to be used.
\item[\code{na.action}] a function which indicates what should happen when
the data contain \code{NA}s.  Defaults to
\code{getOption("na.action")}.
\item[\code{...}] further arguments to be passed to or from methods.
\end{ldescription}
\end{Arguments}
\begin{Details}\relax
The formula interface is only applicable for the 2-sample tests.

If only \code{x} is given, or if both \code{x} and \code{y} are given
and \code{paired} is \code{TRUE}, a Wilcoxon signed rank test of the
null that the distribution of \code{x} (in the one sample case) or of
\code{x-y} (in the paired two sample case) is symmetric about
\code{mu} is performed.

Otherwise, if both \code{x} and \code{y} are given and \code{paired}
is \code{FALSE}, a Wilcoxon rank sum test (equivalent to the
Mann-Whitney test) is carried out.  In this case, the null hypothesis
is that the location of the distributions of \code{x} and \code{y}
differ by \code{mu}.

By default (if \code{exact} is not specified), an exact p-value is
computed if the samples contain less than 50 finite values and there
are no ties.  Otherwise, a normal approximation is used.

Optionally (if argument \code{conf.int} is true), a nonparametric
confidence interval and an estimator for the pseudomedian (one-sample
case) or for the difference of the location parameters \code{x-y} is
computed.  (The pseudomedian of a distribution \eqn{F}{} is the median
of the distribution of \eqn{(u+v)/2}{}, where \eqn{u}{} and \eqn{v}{} are
independent, each with distribution \eqn{F}{}.  If \eqn{F}{} is symmetric,
then the pseudomedian and median coincide.  See Hollander \& Wolfe
(1973), page 34.)  If exact p-values are available, an exact
confidence interval is obtained by the algorithm described in Bauer
(1972), and the Hodges-Lehmann estimator is employed.  Otherwise, the
returned confidence interval and point estimate are based on normal
approximations.\end{Details}
\begin{Value}
A list with class \code{"htest"} containing the following components:
\begin{ldescription}
\item[\code{statistic}] the value of the test statistic with a name
describing it.
\item[\code{parameter}] the parameter(s) for the exact distribution of the
test statistic.
\item[\code{p.value}] the p-value for the test.
\item[\code{null.value}] the location parameter \code{mu}.
\item[\code{alternative}] a character string describing the alternative
hypothesis.
\item[\code{method}] the type of test applied.
\item[\code{data.name}] a character string giving the names of the data.
\item[\code{conf.int}] a confidence interval for the location parameter.
(Only present if argument \code{conf.int = TRUE}.)
\item[\code{estimate}] an estimate of the location parameter.
(Only present if argument \code{conf.int = TRUE}.)
\end{ldescription}
\end{Value}
\begin{References}\relax
Myles Hollander \& Douglas A. Wolfe (1973),
\emph{Nonparametric statistical inference}.
New York: John Wiley \& Sons.
Pages 27--33 (one-sample), 68--75 (two-sample).

David F. Bauer (1972),
Constructing confidence sets using rank statistics.
\emph{Journal of the American Statistical Association}
\bold{67}, 687--690.\end{References}
\begin{SeeAlso}\relax
\code{\Link{kruskal.test}} for testing homogeneity in location
parameters in the case of two or more samples;
\code{\Link{t.test}} for a parametric alternative under normality
assumptions.\end{SeeAlso}
\begin{Examples}
\begin{ExampleCode}
## One-sample test.
## Hollander & Wolfe (1973), 29f.
## Hamilton depression scale factor measurements in 9 patients with
##  mixed anxiety and depression, taken at the first (x) and second
##  (y) visit after initiation of a therapy (administration of a
##  tranquilizer).
x <- c(1.83,  0.50,  1.62,  2.48, 1.68, 1.88, 1.55, 3.06, 1.30)
y <- c(0.878, 0.647, 0.598, 2.05, 1.06, 1.29, 1.06, 3.14, 1.29)
wilcox.test(x, y, paired = TRUE, alternative = "greater")
wilcox.test(y - x, alternative = "less")    # The same.
wilcox.test(y - x, alternative = "less",
            exact = FALSE, correct = FALSE) # H&W large sample
                                            # approximation

## Two-sample test.
## Hollander & Wolfe (1973), 69f.
## Permeability constants of the human chorioamnion (a placental
##  membrane) at term (x) and between 12 to 26 weeks gestational
##  age (y).  The alternative of interest is greater permeability
##  of the human chorioamnion for the term pregnancy.
x <- c(0.80, 0.83, 1.89, 1.04, 1.45, 1.38, 1.91, 1.64, 0.73, 1.46)
y <- c(1.15, 0.88, 0.90, 0.74, 1.21)
wilcox.test(x, y, alternative = "g")        # greater
wilcox.test(x, y, alternative = "greater",
            exact = FALSE, correct = FALSE) # H&W large sample
                                            # approximation

wilcox.test(rnorm(10), rnorm(10, 2), conf.int = TRUE)

## Formula interface.
data(airquality)
boxplot(Ozone ~ Month, data = airquality)
wilcox.test(Ozone ~ Month, data = airquality,
            subset = Month %in% c(5, 8))
\end{ExampleCode}
\end{Examples}

