| 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$double.eps))
lmult(x, object)
Arguments
X |
numeric matrix of transformations. |
Y |
numeric matrix of influence functions. |
ix |
an optional integer vector expanding |
iy |
an optional integer vector expanding |
weights |
an optional integer vector of non-negative case weights. |
subset |
an optional integer vector defining a subset of observations. |
block |
an optional factor defining independent blocks of observations. |
checkNAs |
a logical for switching off missing value checks. This
included switching off checks for suitable values of |
varonly |
a logical asking for variances only. |
nresample |
an integer defining the number of permuted statistics to draw. |
standardise |
a logical asking to standardise the permuted statistics. |
tol |
tolerance for zero variances. |
x |
a contrast matrix to be left-multiplied in case |
object |
an object of class |
Details
The function, after minimal preprocessing, calls the underlying C code
and computes the linear statistic, its expectation and covariance and,
optionally, nresample samples from its permutation distribution.
When both ix and iy are missing, the number of rows of
X and Y is the same, ie the number of observations.
When X is missing and ix a factor, the code proceeds as
if X were a dummy matrix of ix without explicitly
computing this matrix.
Both ix and iy being present means the code treats them
as subsetting vectors for X and Y. Note that ix = 0
or iy = 0 means that the corresponding observation is missing
and the first row or X and Y must be zero.
lmult allows left-multiplication of a contrast matrix when X
was (equivalent to) a factor.
Value
A list.
References
Strasser, H. and Weber, C. (1999). On the asymptotic theory of permutation statistics. Mathematical Methods of Statistics 8(2), 220–250.
Examples
wilcox.test(Ozone ~ Month, data = airquality, subset = Month %in% c(5, 8),
exact = FALSE, correct = FALSE)
aq <- subset(airquality, Month %in% c(5, 8))
X <- as.double(aq$Month == 5)
Y <- as.double(rank(aq$Ozone, na.last = "keep"))
doTest(LinStatExpCov(X, Y))
Cross Tabulation
Description
Efficient weighted cross tabulation of two factors and a block
Usage
ctabs(ix, iy = integer(0), block = integer(0), weights = integer(0),
subset = integer(0), checkNAs = TRUE)
Arguments
ix |
a integer of positive values with zero indicating a missing. |
iy |
an optional integer of positive values with zero indicating a missing. |
block |
an optional blocking factor without missings. |
weights |
an optional vector of case weights, integer or double. |
subset |
an optional integer vector indicating a subset. |
checkNAs |
a logical for switching off missing value checks. |
Details
A faster version of xtabs(weights ~ ix + iy + block, subset).
Value
If block is present, a three-way table. Otherwise,
a one- or two-dimensional table.
Examples
ctabs(ix = 1:5, iy = 1:5, weights = 1:5 / 5)
Permutation Test
Description
Perform permutation test for a linear statistic
Usage
doTest(object, teststat = c("maximum", "quadratic", "scalar"),
alternative = c("two.sided", "less", "greater"), pvalue = TRUE,
lower = FALSE, log = FALSE, PermutedStatistics = FALSE,
minbucket = 10L, ordered = TRUE, maxselect = object$Xfactor,
pargs = GenzBretz())
Arguments
object |
an object returned by |
teststat |
type of test statistic to use. |
alternative |
alternative for scalar or maximum-type statistics. |
pvalue |
a logical indicating if a p-value shall be computed. |
lower |
a logical indicating if a p-value ( |
log |
a logical, if |
PermutedStatistics |
a logical, return permuted test statistics. |
minbucket |
minimum weight in either of two groups for maximally selected statistics. |
ordered |
a logical, if |
maxselect |
a logical, if |
pargs |
arguments as in |
Details
Computes a test statistic, a corresponding p-value and, optionally, cutpoints for maximally selected statistics.
Value
A list.