Vine based distributions

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

Usage

dvine(x, vine, cores = 1, log = FALSE)

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

rvine(n, vine, qrng = FALSE, cores = 1, x_cond = NULL, conditioning_set = NULL)

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 .

log

if TRUE, dvine() returns the log-density instead of the density. The density is accumulated in log space either way and only exponentiated at the end; log = TRUE skips that last step. The joint density is a product over the margins and the vine edges, so it underflows to 0 in high dimensions or under strong dependence while its logarithm is still an ordinary double.

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).

x_cond

optional conditioning values for rvine() on the original data scale. A vector or one-row object is repeated n times; alternatively, supply an n-row matrix or data frame for observation-specific conditioning values. If NULL, rvine() performs unconditional simulation.

conditioning_set

variable indices or names corresponding to the columns of x_cond. When NULL, the columns correspond to the last variables of the current copula order. Discrete left limits are computed internally from the fitted margins.

Value

dvine() gives the density, pvine() gives the distribution function, and rvine() generates unconditional or conditional 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. Margins are evaluated through dmargin(), pmargin(), and qmargin(). Methods are provided for margins fitted by vine() and for the fixed stats::Distributions specifications accepted by 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(stats_margin("norm")), pcs, mat)

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


# evaluate the density and cdf
dvine(x[1, ], vc)
#> [1] 0.3210905
pvine(x[1, ], vc)
#> [1] 1e-04