Strasser-Weber type linear statistics and their expectation and covariance under the independence hypothesis
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$double.eps))
lmult(x, object)A list.
numeric matrix of transformations.
numeric matrix of influence functions.
an optional integer vector expanding X.
an optional integer vector expanding Y.
an optional integer vector of non-negative case weights.
an optional integer vector defining a subset of observations.
an optional factor defining independent blocks of observations.
a logical for switching off missing value checks. This
included switching off checks for suitable values of subset.
Use at your own risk.
a logical asking for variances only.
an integer defining the number of permuted statistics to draw.
a logical asking to standardise the permuted statistics.
tolerance for zero variances.
a contrast matrix to be left-multiplied in case X was a factor.
an object of class "LinStatExpCov".
The function, after minimal preprocessing, calls the underlying C code
and computes the linear statistic, its expectation and covariance and,
optionally, nresample samples from its permutation distribution.
When both ix and iy are missing, the number of rows of
X and Y is the same, ie the number of observations.
When X is missing and ix a factor, the code proceeds as
if X were a dummy matrix of ix without explicitly
computing this matrix.
Both ix and iy being present means the code treats them
as subsetting vectors for X and Y. Note that ix = 0
or iy = 0 means that the corresponding observation is missing
and the first row or X and Y must be zero.
lmult allows left-multiplication of a contrast matrix when X
was (equivalent to) a factor.
Strasser, H. and Weber, C. (1999). On the asymptotic theory of permutation statistics. Mathematical Methods of Statistics 8(2), 220--250.
wilcox.test(Ozone ~ Month, data = airquality, subset = Month %in% c(5, 8),
exact = FALSE, correct = FALSE)
aq <- subset(airquality, Month %in% c(5, 8))
X <- as.double(aq$Month == 5)
Y <- as.double(rank(aq$Ozone, na.last = "keep"))
doTest(LinStatExpCov(X, Y))
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