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/in.progress/auteur/man/randomization.test.Rd

http://github.com/eastman/auteur
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 1\name{randomization.test}
 2\alias{randomization.test}
 3\title{statistical comparison of sets of values by randomization}
 4\description{Compares means by bootstrap resampling of differences between empirical distributions}
 5\usage{
 6randomization.test(obs = obs, exp = exp, mu = 0, iter = 10000, two.tailed = FALSE)
 7}
 8%- maybe also 'usage' for other objects documented here.
 9\arguments{
10  \item{obs}{a vector of numeric values}
11  \item{exp}{a vector of numeric values}
12  \item{mu}{the true difference in means}
13  \item{iter}{number of randomization comparisons to perform}
14  \item{two.tailed}{as default, the test is performed under a one-tailed assumption; if \code{two.tailed=FALSE}, probability values associated with either tail of the comparison distribution are returned, 
15otherwise, a two-tailed result is returned}
16}
17\details{
18If a single value is supplied for \code{obs}, this test equates to finding the quantile in \code{exp} in which \code{obs} would be found (under a one-tailed test); 
19see \bold{Examples} and also \code{\link[stats]{ecdf}}}
20\value{
21A list, whose contents are determined by the above argument: 
22  \item{unnamed value}{if \code{two.tailed=TRUE}, this is the two-tailed p-value}
23  \item{diffs}{the full resampling distribution of differences between \code{obs} and \code{exp}, given \code{mu} }
24  \item{greater}{if \code{two.tailed=FALSE}, this is the p-value associated with the righthand tail}
25  \item{lesser}{if \code{two.tailed=FALSE}, this is the p-value associated with the lefthand tail}
26}
27\author{JM Eastman}
28\examples{
29
30# a comparison between two distributions
31a=rnorm(n=1000, mean=1, sd=0.5)
32b=rnorm(n=1000, mean=0, sd=1)
33randomization.test(obs=a, exp=b, two.tailed=FALSE)
34
35# a comparison of a single value to a normal distribution
36a=3
37b=rnorm(n=1000, mean=0, sd=1)
38randomization.test(obs=a, exp=b, two.tailed=FALSE)
39
40# compare above result with ecdf(), in which we compute an empirical 
41f=ecdf(b)
42print(1-f(a))		# analogous to a one-tailed test as above
43}