TestIndCopula {MixedIndTests}

Statistics and P-values for a test of independence between random variables

Description

This function computes Cramer-von Mises statistics and their combination for a tests of independence between random variables with arbitrary distributions. The P-values are computed using Gaussian multipliers.

Usage

TestIndCopula(
  x,
  trunc.level = 2,
  B = 1000,
  par = FALSE,
  ncores = 2,
  graph = FALSE
)

Arguments

x

Data matrix

trunc.level

Only subsets of cardinality <= trunc.level (default=2) are considered for the Moebius statistics.

B

Number of multipliers samples (default = 1000)

par

Set to TRUE if one prefers paraller computing (slower)

ncores

Number of cores for parallel computing (default is 2)

graph

Set to TRUE if one wants the dependogram of P-values for the Moebius statistics

Value

stat

List of Cramer-von Mises statistics cvm, Sn from the multilinear copula, and test combinations Tn and Tn2 (only pairs)

pvalue

Approximated P-values for the tests using Gaussian multipliers

card

Cardinaly of the subsets for the Moebius statistics

subsets

Subsets for the Moebius statistics

References

Genest, Neslehova, Remillard & Murphy (2019). Testing for independence in arbitrary distributions

Examples

x <- matrix(rnorm(250),ncol=5)
out <-TestIndCopula(x)