corc: Function to estimate copula using ranks and sub-sampling

In subrank: Computes Copula using Ranks and Subsampling

Function to estimate copula using ranks and sub-sampling

Description

Takes a sample, its dimension, a sub-sample size, and returns a discrete copula.

Usage

corc(dataframe, varnames, subsampsize, nbsafe=5,mixties=FALSE,nthreads=2)

Arguments

dataframe	a data frame, containing the observations
varnames	the name of the variables we want to estimate the dependence between
subsampsize	the sub-sample size
nbsafe	the ratio between the number of sub-samples and the cardinality of the discretized copula.
mixties	if TRUE, put equal weight on tied values, using random permutations
nthreads	number of number of threads, assumed to be strictly positive. For "full throttle" computations, consider using parallel::detectCores()

Value

cop	an array representing the discretized copula
ties	the number of sub-samples with a tie
nsubsampreal	the effective number of sub-samples drawn
varnames	the name of the variables studied
nnm	the number of observations without missing values

Author(s)

Jerome Collet

Examples

lon <- 30
a <- 2
x <- rnorm(lon)
y = a*x^2+rnorm(lon)
datatable = as.data.frame(cbind(x,y))
c=corc(datatable,c("x","y"),8)
c
sum(c$cop)

subrank documentation built on April 6, 2023, 1:11 a.m.