Description Usage Arguments Details Value References Examples

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

1 2 3 4 5 6 |

`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 |

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.

A list.

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

1 2 3 4 5 6 7 8 | ```
wilcox.test(Ozone ~ Month, data = airquality,
subset = Month %in% c(5, 8))
aq <- subset(airquality, Month %in% c(5, 8))
X <- as.double(aq$Month == 5)
Y <- as.double(rank(aq$Ozone))
doTest(LinStatExpCov(X, Y))
``` |

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