impossible_data {cort}R Documentation

Dataset impossible_data

Description

We simulate from a density inside the piecewise linear copula class, by applying the function:

h(u) = (u_1, \frac{u_2}{2} + \frac{1}{2}I_{u_1 \notin (\frac{1}{3}, \frac{2}{3})})

to a 200x2 uniform sample, and taking ranks.

Usage

impossible_data

Format

A matrix with 200 rows and 2 columns

The example section below gives the code to re-generate this data if needed.

Details

This dataset is studied in O. Laverny, V. Maume-Deschamps, E. Masiello and D. Rullière (2020).

References

Laverny O, Maume-Deschamps V, Masiello E, Rullière D (2020). “Dependence Structure Estimation Using Copula Recursive Trees.” arXiv preprint arXiv:2005.02912.

Examples

set.seed(seed = 12, kind = "Mersenne-Twister", normal.kind = "Inversion")
x = matrix(runif(400),200,2)
x = t(apply(x, 1,function(u){
  if(u[1]< 1/3){
    u[2] = 1/2 + u[2]/2
  } else{ if(u[1]<2/3){
    u[2] = u[2]/2
  } else {
    u[2] = 1/2 + u[2]/2
  }}
  return(u)
}))
impossible_data = apply(x,2,function(x){return(rank(x,ties.method = "max"))})/(201)


[Package cort version 0.3.2 Index]