vinecop_dist: Vine copula models

In rvinecopulib: High Performance Algorithms for Vine Copula Modeling

Vine copula models

Description

Create custom vine copula models by specifying the pair-copulas, structure, and variable types.

Usage

vinecop_dist(pair_copulas, structure, var_types = rep("c", dim(structure)[1]))

Arguments

pair_copulas	A nested list of 'bicop_dist()' objects, where pair_copulas[[t]][[e]] corresponds to the pair-copula at edge e in tree t.
structure	an rvine_structure object, namely a compressed representation of the vine structure, or an object that can be coerced into one (see rvine_structure() and as_rvine_structure()). The dimension must be length(pair_copulas[[1]]) + 1; structure = NA performs automatic selection based on Dissman's algorithm. See Details for partial selection of the structure.
var_types	variable types, a length d vector; e.g., c("c", "c") for two continuous variables, or c("c", "d") for first variable continuous and second discrete.

Value

Object of class vinecop_dist, i.e., a list containing:

• pair_copulas, a list of lists. Each element of pair_copulas corresponds to a tree, which is itself a list of bicop_dist() objects.

• structure, a compressed representation of the vine structure, or an object that can be coerced into one (see rvine_structure() and as_rvine_structure()).

• npars, a numeric with the number of (effective) parameters.

• var_types the variable types.

See Also

rvine_structure(), rvine_matrix(), vinecop(), plot.vinecop_dist(), contour.vinecop_dist(), dvinecop(), pvinecop(), rvinecop()

Examples

# specify pair-copulas
bicop <- bicop_dist("bb1", 90, c(3, 2))
pcs <- list(
  list(bicop, bicop), # pair-copulas in first tree
  list(bicop) # pair-copulas in second tree
)

# specify R-vine matrix
mat <- matrix(c(1, 2, 3, 1, 2, 0, 1, 0, 0), 3, 3)

# set up vine copula model
vc <- vinecop_dist(pcs, mat)

# visualization
plot(vc)
contour(vc)

# simulate from the model
pairs(rvinecop(200, vc))

rvinecopulib documentation built on March 7, 2023, 6:20 p.m.