funcdep_data {cort} | R Documentation |
This dependence structure is constructed by applying the function :
h(u_1,u_2,u_3) = (u_{1},\sin(2\pi u_{1})-\frac{u_{2}}{\pi},(1+\frac{u_{3}}{\pi^{2}})(\frac{u_{3}}{2} I_{\frac{1}{4}\ge u_1}-\sin(\pi^{x_{1}}) I_{\frac{1}{4} < u_{1}}))
to uniformly drawn 3-dimensional random vectors. The dataset is the ranks of h(u)
.
funcdep_data
A matrix with 500 rows and 3 columns
The example section below gives the code to re-generate this data if needed.
This dataset is studied in O. Laverny, V. Maume-Deschamps, E. Masiello and D. Rullière (2020).
Laverny O, Maume-Deschamps V, Masiello E, Rullière D (2020). “Dependence Structure Estimation Using Copula Recursive Trees.” arXiv preprint arXiv:2005.02912.
set.seed(seed = 12,kind = "Mersenne-Twister",normal.kind = "Inversion")
x = matrix(runif(1500),500,3)
x[,2] = sin(2*pi*x[,1])-x[,2]/pi
x[,3] = (x[,3]*(x[,1]<1/4)/2 - sin(pi**(x[,1]))*(x[,1]>1/4))*(1+x[,3]/(pi^2))
funcdep_data = apply(x,2,function(x){return(rank(x,ties.method = "max"))})/(501)