Revision 6b9cee07bdce93e338e4b25e3c60aa19abbbc50e authored by Torsten Hothorn on 31 March 2009, 00:00:00 UTC, committed by Gabor Csardi on 31 March 2009, 00:00:00 UTC
1 parent 59aef90
pvalue-methods.Rd
\name{pvalue-methods}
\docType{methods}
\alias{pvalue}
\alias{pvalue-methods}
\alias{pvalue,NullDistribution-method}
\alias{pvalue,IndependenceTest-method}
\alias{pvalue,ScalarIndependenceTest-method}
\alias{pvalue,MaxTypeIndependenceTest-method}
\alias{pvalue,QuadTypeIndependenceTest-method}
\title{ Extract P-Values }
\description{
Extracts the p-value from objects representing null distributions of
independence tests.
}
\usage{
pvalue(object, ...)
}
\arguments{
\item{object}{an object inheriting from class
\code{\link{IndependenceTest-class}}.}
\item{\dots}{additional arguments: \code{method},
a character specifying the type of
adjustment (\code{global}, \code{single-step}, \code{step-down} or \code{discrete})
should be used. The default is \code{global}.}
}
\section{Methods}{
\describe{
\item{pvalue}{extracts the p-value from the specified object.}
}
}
\details{
Univariate p-values for maximum-type statistics come with associated 99\%
confidence interval when resampling was used to determine the null
distribution (which may be the case even when \code{distribution =
"asypmtotic"} was used).
By default, a global p-value is returned. When \code{method =
"single-step"}, adjusted p-values are obtained from a
single-step max-T procedure
(Westfall & Young, 1993, algorithm 2.5 and formula 2.8). Note that the
minimum of the adjusted p-values always controls the familywise error
rate (FWER) but the maximum type I error, i.e. the error for
each of the individual tests, is only controlled when the subset
pivotality condition holds.
When \code{method = "step-down"} the free step-down resampling method
(algorithm 2.8 and formula 2.8 in Westfall & Young, 1993) is used, the above
comments apply as well.
With \code{method = "discrete"}, the Bonferroni adjustment as suggested by
Westfall & Wolfinger (1997) with improvements for highly discrete
permutation distributions is available, however, without taking
correlations between the test statistics into account. Here, the p-values are
valid even without assuming subset pivotality.
}
\references{
Peter H. Westfall \& S. Stanley Young (1993).
\emph{Resampling-based Multiple Testing}.
New York: John Wiley & Sons.
Peter H. Westfall \& Russell D. Wolfinger (1997).
Multiple tests with discrete distributions.
\emph{The American Statistician} \bold{51}, 3--8.
}
\examples{
### artificial 2-sample problem
df <- data.frame(y = rnorm(20), x = gl(2, 10))
### Ansari-Bradley test
at <- ansari_test(y ~ x, data = df, distribution = "exact")
at
pvalue(at)
### bivariate 2-sample problem
df <- data.frame(y1 = rnorm(20) + c(rep(0, 10), rep(1, 10)),
y2 = rnorm(20),
x = gl(2, 10))
it <- independence_test(y1 + y2 ~ x, data = df,
distribution = approximate(B = 9999))
pvalue(it, method = "single-step")
pvalue(it, method = "step-down")
pvalue(it, method = "discrete")
}
\keyword{methods}
\keyword{htest}
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