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In multivariance: Measuring Multivariate Dependence Using Distance Multivariance

Description Usage Arguments Details Value See Also Examples

View source: R/multivariance-functions.R

Computes simultaneously multivariance, total multivariance, 2-multivariance and 3-multivariance.

1	multivariances.all(x, vec = NA, Nscale = TRUE, squared = TRUE, ...)

`x`	either a data matrix or a list of doubly centered distance matrices
`vec`	if x is a matrix, then this indicates which columns are treated together as one sample; if x is a list, these are the indexes for which the multivariance is calculated. The default is all columns and all indexes, respectively.
`Nscale`	if `TRUE` the multivariance is scaled up by the sample size (and thus it is exactly as required for the test of independence)
`squared`	if `FALSE` it returns the actual multivariance, otherwise the squared multivariance (less computation)
`...`	these are passed to `cdms` (which is only invoked if `x` is a matrix)

The computation is faster than the separate computations.

Returns a vector with multivariance, total.multivariance, 2-multivariance and 3-multivariance

multivariance, total.multivariance, m.multivariance

x = coins(100,k = 3)
multivariances.all(x)
# yields the same as:
multivariance(x)
total.multivariance(x)
m.multivariance(x,m=2)
m.multivariance(x,m=3)