Random sampling of R-Vine matrices
Sample R-Vine matrices based on the algorithm of Joe et al. (2011).
RVineMatrixSample(d, size = 1, naturalOrder = FALSE)
d |
Dimension of the R-Vine matrices. |
size |
Number of matrices to sample. |
naturalOrder |
Should the matrices be in the natural order
(default: |
A list of length size with each element containing one
R-Vine matrix.
For some reason, our implementation of Joe et al.'s algorithm always returns a star in the first tree. To fix this, we sample a vine matrix of dimension d + 1 and remove the first tree afterwards
Thibault Vatter
Joe H, Cooke RM and Kurowicka D (2011). Regular vines: generation algorithm and number of equivalence classes. In Dependence Modeling: Vine Copula Handbook, pp 219–231. World Scientific, Singapore.
# Matrix and sample sizes d <- 10 size <- 5 # Sample R-vine matrices RVM <- RVineMatrixSample(d, size) sapply(RVM, RVineMatrixCheck) # Sample R-vine matrices in the natural order RVM <- RVineMatrixSample(d, size, naturalOrder = TRUE) sapply(RVM, RVineMatrixCheck)
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