Simulation from a gamVine-class object
gamVineSimulate(n, GVC, U = NULL, newdata = NULL)number of d-dimensional observations to simulate.
A gamVine object.
If not NULL, U is an (N,d)-matrix of U[0,1] random
variates to be transformed to the copula sample.
If not NULL, which is mandatory when
the attribute covariates from GVC is not NA,
newdata is a data frame containing the values of
the model covariates at which simulations are required.
A matrix of data simulated from the given
gamVine object.
require(VineCopula)
#> Loading required package: VineCopula
#>
#> Attaching package: ‘VineCopula’
#> The following object is masked from ‘package:copula’:
#>
#> pobs
## Example adapted from RVineSim
## Define 5-dimensional R-vine tree structure matrix
Matrix <- c(
5, 2, 3, 1, 4,
0, 2, 3, 4, 1,
0, 0, 3, 4, 1,
0, 0, 0, 4, 1,
0, 0, 0, 0, 1
)
Matrix <- matrix(Matrix, 5, 5)
## Define R-vine pair-copula family matrix
family <- c(
0, 1, 3, 4, 4,
0, 0, 3, 4, 1,
0, 0, 0, 4, 1,
0, 0, 0, 0, 3,
0, 0, 0, 0, 0
)
family <- matrix(family, 5, 5)
## Define R-vine pair-copula parameter matrix
par <- c(
0, 0.2, 0.9, 1.5, 3.9,
0, 0, 1.1, 1.6, 0.9,
0, 0, 0, 1.9, 0.5,
0, 0, 0, 0, 4.8,
0, 0, 0, 0, 0
)
par <- matrix(par, 5, 5)
## Define second R-vine pair-copula parameter matrix
par2 <- matrix(0, 5, 5)
## Define RVineMatrix object
RVM <- RVineMatrix(
Matrix = Matrix, family = family,
par = par, par2 = par2,
names = c("V1", "V2", "V3", "V4", "V5")
)
## Convert to gamVine object
GVC <- RVM2GVC(RVM)
## U[0,1] random variates to be transformed to the copula sample
n <- 1e2
d <- 5
U <- matrix(runif(n * d), nrow = n)
## The output of gamVineSimulate correspond to that of RVineSim
sampleRVM <- RVineSim(n, RVM, U)
sampleGVC <- gamVineSimulate(n, GVC, U)
all.equal(sampleRVM, sampleGVC)
#> [1] TRUE
## Fit the two models and compare the estimated parameter
fitRVM <- RVM2GVC(RVineSeqEst(sampleRVM, RVM))
fitGVC <- gamVineSeqFit(sampleGVC, GVC)
all.equal(
simplify2array(attr(fitRVM, "model")),
simplify2array(attr(fitGVC, "model"))
)
#> [1] TRUE