Vine based distributions

Density, distribution function and random generation for the vine based distribution.

dvine(x, vine, cores = 1)

pvine(x, vine, n_mc = 10^4, cores = 1)

rvine(n, vine, qrng = FALSE, cores = 1)

Arguments

x

evaluation points, either a length d vector or a d-column matrix, where d is the number of variables in the vine.

vine

an object of class "vine_dist".

cores

number of cores to use; if larger than one, computations are done in parallel on cores batches .

n_mc

number of samples used for quasi Monte Carlo integration.

n

number of observations.

qrng

if TRUE, generates quasi-random numbers using the multivariate Generalized Halton sequence up to dimension 300 and the Generalized Sobol sequence in higher dimensions (default qrng = FALSE).

Value

dvine() gives the density, pvine() gives the distribution function, and rvine() generates random deviates.

The length of the result is determined by n for rvine(), and the number of rows in u for the other functions.

The vine object is recycled to the length of the result.

Details

See vine for the estimation and construction of vine models. Here, the density, distribution function and random generation for the vine distributions are standard.

The functions are based on dvinecop(), pvinecop() and rvinecop() for vinecop objects, and either kde1d::dkde1d(), kde1d::pkde1d() and kde1d::qkde1d() for estimated vines (i.e., output of vine()), or the standard d/p/q-xxx from stats::Distributions for custom vines (i.e., output of vine_dist()).

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
)

# set up vine copula model
mat <- rvine_matrix_sim(3)
vc <- vine_dist(list(distr = "norm"), pcs, mat)

# simulate from the model
x <- rvine(200, vc)
pairs(x)


# evaluate the density and cdf
dvine(x[1, ], vc)
#> [1] 1.99482
pvine(x[1, ], vc)
#> [1] 1e-04