R-vine structure

R-vine structures are compressed representations encoding the tree structure of the vine, i.e. the conditioned/conditioning variables of each edge. The functions [cvine_structure()] or [dvine_structure()] give a simpler way to construct C-vines (every tree is a star) and D-vines (every tree is a path), respectively (see Examples).

Usage

rvine_structure(order, struct_array = list(), is_natural_order = FALSE)

cvine_structure(order, trunc_lvl = Inf)

dvine_structure(order, trunc_lvl = Inf)

rvine_matrix(matrix)

Arguments

order

a vector of positive integers.

struct_array

a list of vectors of positive integers. The vectors represent rows of the r-rvine structure and the number of elements have to be compatible with the order vector. If empty, the model is 0-truncated.

is_natural_order

whether struct_array is assumed to be provided in natural order already (a structure is in natural order if the anti- diagonal is 1, .., d from bottom left to top right).

trunc_lvl

the truncation level

matrix

an R-vine matrix, see Details.

Value

Either an rvine_structure or an rvine_matrix.

Details

The R-vine structure is essentially a lower-triangular matrix/triangular array, with a notation that differs from the one in the VineCopula package. An example array is

4 4 4 4
3 3 3
2 2
1

which encodes the following pair-copulas:

tree	edge	pair-copulas
0	0	(1, 4)
	1	(2, 4)
	2	(3, 4)
1	0	(1, 3; 4)
	1	(2, 3; 4)
2	0	(1, 2; 3, 4)

An R-vine structure can be converted to an R-vine matrix using as_rvine_matrix(), which encodes the same model with a square matrix filled with zeros. For instance, the matrix corresponding to the structure above is:

4 4 4 4
3 3 3 0
2 2 0 0
1 0 0 0

Similarly, an R-vine matrix can be converted to an R-vine structure using as_rvine_structure().

Denoting by M[i, j] the array entry in row i and column j (the pair-copula index for edge e in tree t of a d dimensional vine is (M[d + 1 - e, e], M[t, e]; M[t - 1, e], ..., M[1, e]). Less formally,

1. Start with the counter-diagonal element of column e (first conditioned variable).

2. Jump up to the element in row t (second conditioned variable).

3. Gather all entries further up in column e (conditioning set).

Internally, the diagonal is stored separately from the off-diagonal elements, which are stored as a triangular array. For instance, the off-diagonal elements off the structure above are stored as

4 4 4
3 3
2

for the structure above. The reason is that it allows for parsimonious representations of truncated models. For instance, the 2-truncated model is represented by the same diagonal and the following truncated triangular array:

4 4 4
3 3

A valid R-vine structure or matrix must satisfy several conditions which are checked when rvine_structure(), rvine_matrix(), or some coercion methods (see as_rvine_structure() and as_rvine_matrix() are called:

1. It can only contain numbers between 1 and d (and additionally zeros for R-vine matrices).

2. The anti-diagonal must contain the numbers 1, ..., d.

3. The anti-diagonal entry of a column must not be contained in any column further to the right.

4. The entries of a column must be contained in all columns to the left.

5. The proximity condition must hold: For all t = 1, ..., d - 2 and e = 1, ..., d - t there must exist an index j > d, such that (M[t, e], {M[1, e], ..., M[t - 1, e]}) equals either (M[d + 1 - j, j], {M[1, j], ..., M[t - 1, j]}) or (M[t - 1, j], {M[d + 1 - j, j], M[1, j], ..., M[t - 2, j]}).

Condition 5 already implies conditions 2-4, but is more difficult to check by hand.

See also

as_rvine_structure(), as_rvine_matrix(), plot.rvine_structure(), plot.rvine_matrix(), rvine_structure_sim(), rvine_matrix_sim()

Examples

# R-vine structures can be constructed from the order vector and struct_array
rvine_structure(order = 1:4, struct_array = list(
  c(4, 4, 4),
  c(3, 3),
  2
))
#> 4-dimensional R-vine structure ('rvine_structure')
#> 4 4 4 4
#> 3 3 3  
#> 2 2    
#> 1      

# R-vine matrices can be constructed from standard matrices
mat <- matrix(c(4, 3, 2, 1, 4, 3, 2, 0, 4, 3, 0, 0, 4, 0, 0, 0), 4, 4)
rvine_matrix(mat)
#> 4-dimensional R-vine matrix ('rvine_matrix')
#> 4 4 4 4
#> 3 3 3  
#> 2 2    
#> 1      

# coerce to R-vine structure
str(as_rvine_structure(mat))
#> List of 4
#>  $ order       : num [1:4] 1 2 3 4
#>  $ struct_array:List of 3
#>   ..$ : int [1:3] 4 4 4
#>   ..$ : int [1:2] 3 3
#>   ..$ : int 2
#>  $ d           : Named num 4
#>   ..- attr(*, "names")= chr "dim"
#>  $ trunc_lvl   : Named num 3
#>   ..- attr(*, "names")= chr "trunc_lvl"
#>  - attr(*, "class")= chr [1:2] "rvine_structure" "list"

# truncate and construct the R-vine matrix
mat[3, 1] <- 0
rvine_matrix(mat)
#> 4-dimensional R-vine matrix ('rvine_matrix'), 2-truncated
#> 4 4 4 4
#> 3 3 3  
#>   2    
#> 1      

# or use directly the R-vine structure constructor
rvine_structure(order = 1:4, struct_array = list(
  c(4, 4, 4),
  c(3, 3)
))
#> 4-dimensional R-vine structure ('rvine_structure'), 2-truncated
#> 4 4 4 4
#> 3 3 3  
#>   2    
#> 1      

# throws an error
mat[3, 1] <- 5
try(rvine_matrix(mat))
#> Error : not a valid R-vine array: the upper left triangle can only contain numbers between 1 and d (number of variables).

# C-vine structure
cvine <- cvine_structure(1:5)
cvine
#> 5-dimensional R-vine structure ('rvine_structure')
#> 5 5 5 5 5
#> 4 4 4 4  
#> 3 3 3    
#> 2 2      
#> 1        
plot(cvine)
#> Error in plot.vinecop_dist(vinecop_dist(pcs, x), ...): The 'ggraph' package must be installed to plot.

# D-vine structure
dvine <- dvine_structure(c(1, 4, 2, 3, 5))
dvine
#> 5-dimensional R-vine structure ('rvine_structure')
#> 4 2 3 5 5
#> 2 3 5 3  
#> 3 5 2    
#> 5 4      
#> 1        
plot(dvine)
#> Error in plot.vinecop_dist(vinecop_dist(pcs, x), ...): The 'ggraph' package must be installed to plot.