cma_separation: CMA Separation Step

In Reckziegel/CMA: Copula Marginal Algorithm (CMA)

CMA Separation Step

Description

First CMA step: decomposes the the pure "individual" features in the marginals from the pure "joint" information available in the copulas.

Usage

cma_separation(x, p = NULL)

## Default S3 method:
cma_separation(x, p = NULL)

## S3 method for class 'matrix'
cma_separation(x, p = NULL)

## S3 method for class 'xts'
cma_separation(x, p = NULL)

## S3 method for class 'data.frame'
cma_separation(x, p = NULL)

## S3 method for class 'tbl'
cma_separation(x, p = NULL)

Arguments

x	A rectangular (non-tidy) data structure.
p	A probability vector. If NULL (the default) the standard 1 over N is used.

Details

Arguments x and p must have the same size.

Value

An S3 list of the cma class that contains three elements: marginal, cdf and copula.

See Also

cma_combination

Examples

set.seed(123)

margins <- matrix(stats::rnorm(30), ncol = 3)
colnames(margins) <- c("a", "b", "c")

cma <- cma_separation(x = margins)
cma

# Access the elements with `$`
cma$copula
cma$marginal
cma$cdf

Reckziegel/CMA documentation built on July 13, 2022, 10:31 p.m.