Description Usage Arguments Value Note Author(s) References See Also Examples
View source: R/CDVineCopulaConditional.r
Provides a ranking of the C- and D- vines which allow for conditional
sampling, under the condition discussed in the descriprion of CDVineCondFit
.
1 2 3 |
data |
An |
Nx |
Number of conditioning variables. |
treecrit |
Character indicating the criteria used to select the vine. All possible vines are fitted trough
the function |
selectioncrit |
Character indicating the criterion for pair-copula selection.
Possible choices are |
familyset |
"Integer vector of pair-copula families to select from. The vector has to include at least one
pair-copula family that allows for positive and one that allows for negative
dependence. Not listed copula families might be included to better handle
limit cases. If |
type |
Type of vine to be fitted: |
indeptest |
"Logical; whether a hypothesis test for the independence of
|
level |
numeric; significance level of the independence test (default:
|
se |
Logical; whether standard errors are estimated (default: |
rotations |
logical; if |
method |
indicates the estimation method: either maximum
likelihood estimation ( |
table
A table with the ranking of the vines, with vine index i
,
values of the selected treecrit
and vine type
(1 for "CVine" and 2 for D-Vine).
vines
A list where the element [[i]]
is an RVineMatrix
object corresponding to
the i
-th vine in the ranking shown in table
.
Each RVineMatrix
object containes the selected families ($family
) as well as sequentially
estimated parameters stored in $par
and $par2
. Details about RVineMatrix
objects are given in
the documentation file of the VineCopula
package).
The fit of each model is performed via the function RVineCopSelect
of the package VineCopula
.
"The object is augmented by the following information about the fit:
se, se2
standard errors for the parameter estimates (if se = TRUE
; note that these are only
approximate since they do not account for the sequential nature of the estimation
nobs
number of observations
logLik, pair.logLik
log likelihood (overall and pairwise)
AIC, pair.AIC
Aikaike's Informaton Criterion (overall and pairwise)
BIC, pair.BIC
Bayesian's Informaton Criterion (overall and pairwise)
emptau
matrix of empirical values of Kendall's tau
p.value.indeptest
matrix of p-values of the independence test.
For a comprehensive summary of the vine copula model, use
summary(object)
; to see all its contents, use str(object)
." (VineCopula Documentation, version 2.1.1, pp. 103)
Emanuele Bevacqua
Bevacqua, E., Maraun, D., Hobaek Haff, I., Widmann, M., and Vrac, M.: Multivariate statistical modelling of compound events via pair-copula constructions: analysis of floods in Ravenna (Italy), Hydrol. Earth Syst. Sci., 21, 2701-2723, https://doi.org/10.5194/hess-21-2701-2017, 2017. [link] [link]
Aas, K., Czado, C., Frigessi, A. and Bakken, H.: Pair-copula constructions of multiple dependence, Insurance: Mathematics and Economics, 44(2), 182-198, <doi:10.1016/j.insmatheco.2007.02.001>, 2009. [link]
Ulf Schepsmeier, Jakob Stoeber, Eike Christian Brechmann, Benedikt Graeler, Thomas Nagler and Tobias Erhardt (2017). VineCopula: Statistical Inference of Vine Copulas. R package version 2.1.1. [link]
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | # Read data
data(dataset)
data <- dataset$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")
# Rank the possible D-Vines according to the AIC
## Not run:
Ranking <- CDVineCondRank(data,Nx=3,"AIC",type="DVine")
Ranking$table
# tree AIC type
# 1 1 -292.8720 2
# 2 2 -290.2941 2
# 3 3 -288.5719 2
# 4 4 -288.2496 2
# 5 5 -287.8006 2
# 6 6 -285.8503 2
# 7 7 -282.2867 2
# 8 8 -278.9371 2
# 9 9 -275.8339 2
# 10 10 -272.9459 2
# 11 11 -271.1526 2
# 12 12 -270.5269 2
Ranking$vines[[1]]$AIC
# [1] -292.8720
summary(Ranking$vines[[1]])
## End(Not run)
|
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