CDVineCondListMatrices {CDVineCopulaConditional} | R Documentation |
Provides a list of the C- and D- vines which allow for conditional
sampling, under the condition discussed in the descriprion of CDVineCondFit
.
CDVineCondListMatrices(data, Nx, type = "CVine-DVine")
data |
An |
Nx |
Number of conditioning variables. |
type |
Type of vine to be considered: |
Listes of matrices describing C- ($CVine
) and D- ($DVine
) Vines.
Each matrix corresponds to a vine, according to the same notation used for RVineMatrix
objects (for further details about RVineMatrix
objects see the documentation file of the VineCopula
package).
The index i
in the matrix corresponds to the variable in the i-th column of data
.
Emanuele Bevacqua
Bevacqua, E., Maraun, D., Hobaek Haff, I., Widmann, M., and Vrac, M.: Multivariate statistical modelling of compound events via pair-copula constructions: analysis of floods in Ravenna (Italy), Hydrol. Earth Syst. Sci., 21, 2701-2723, https://doi.org/10.5194/hess-21-2701-2017, 2017. [link] [link]
Aas, K., Czado, C., Frigessi, A. and Bakken, H.: Pair-copula constructions of multiple dependence, Insurance: Mathematics and Economics, 44(2), 182-198, <doi:10.1016/j.insmatheco.2007.02.001>, 2009. [link]
# Read data
data(dataset)
data <- dataset$data[1:100,1:5]
# Define the variables Y and X. X are the conditioning variables,
# which have to be positioned in the last columns of the data.frame
colnames(data) <- c("Y1","Y2","X3","X4","X5")
# List possible D-Vines:
ListVines <- CDVineCondListMatrices(data,Nx=3,"DVine")
ListVines$DVine
# List possible C-Vines:
ListVines <- CDVineCondListMatrices(data,Nx=3,"CVine")
ListVines$CVine
# List possible C- and D-Vines:
ListVines <- CDVineCondListMatrices(data,Nx=3,"CVine-DVine")
ListVines