Title:	Linear Test Statistics for Permutation Inference
Date:	2023-09-26
Version:	1.0-10
Description:	Basic infrastructure for linear test statistics and permutation inference in the framework of Strasser and Weber (1999) https://epub.wu.ac.at/102/. This package must not be used by end-users. CRAN package 'coin' implements all user interfaces and is ready to be used by anyone.
Depends:	R (≥ 3.4.0)
Suggests:	coin
Imports:	stats, mvtnorm
LinkingTo:	mvtnorm
NeedsCompilation:	yes
License:	GPL-2
Packaged:	2023-09-27 09:57:53 UTC; hothorn
Author:	Torsten Hothorn [aut, cre], Henric Winell [aut]
Maintainer:	Torsten Hothorn <Torsten.Hothorn@R-project.org>
Repository:	CRAN
Date/Publication:	2023-09-27 10:30:07 UTC

Linear Statistics with Expectation and Covariance

Description

Strasser-Weber type linear statistics and their expectation and covariance under the independence hypothesis

Usage

LinStatExpCov(X, Y, ix = NULL, iy = NULL, weights = integer(0),
              subset = integer(0), block = integer(0), checkNAs = TRUE,
              varonly = FALSE, nresample = 0, standardise = FALSE,
              tol = sqrt(.Machine$double.eps))
lmult(x, object)

Arguments

Details

The function, after minimal preprocessing, calls the underlying C code and computes the linear statistic, its expectation and covariance and, optionally, nresample samples from its permutation distribution.

When both ix and iy are missing, the number of rows of X and Y is the same, ie the number of observations.

When X is missing and ix a factor, the code proceeds as if X were a dummy matrix of ix without explicitly computing this matrix.

Both ix and iy being present means the code treats them as subsetting vectors for X and Y. Note that ix = 0 or iy = 0 means that the corresponding observation is missing and the first row or X and Y must be zero.

lmult allows left-multiplication of a contrast matrix when X was (equivalent to) a factor.

Value

A list.

References

Strasser, H. and Weber, C. (1999). On the asymptotic theory of permutation statistics. Mathematical Methods of Statistics 8(2), 220–250.

Examples

wilcox.test(Ozone ~ Month, data = airquality, subset = Month %in% c(5, 8),
            exact = FALSE, correct = FALSE)

aq <- subset(airquality, Month %in% c(5, 8))
X <- as.double(aq$Month == 5)
Y <- as.double(rank(aq$Ozone, na.last = "keep"))
doTest(LinStatExpCov(X, Y))

Cross Tabulation

Description

Efficient weighted cross tabulation of two factors and a block

Usage

ctabs(ix, iy = integer(0), block = integer(0), weights = integer(0),
      subset = integer(0), checkNAs = TRUE)

Arguments

Details

A faster version of xtabs(weights ~ ix + iy + block, subset).

Value

If block is present, a three-way table. Otherwise, a one- or two-dimensional table.

Examples

ctabs(ix = 1:5, iy = 1:5, weights = 1:5 / 5)

Permutation Test

Description

Perform permutation test for a linear statistic

Usage

doTest(object, teststat = c("maximum", "quadratic", "scalar"),
       alternative = c("two.sided", "less", "greater"), pvalue = TRUE,
       lower = FALSE, log = FALSE, PermutedStatistics = FALSE,
       minbucket = 10L, ordered = TRUE, maxselect = object$Xfactor,
       pargs = GenzBretz())

Arguments

Details

Computes a test statistic, a corresponding p-value and, optionally, cutpoints for maximally selected statistics.

Value

A list.