CDVineCondRank {CDVineCopulaConditional}  R Documentation 
Provides a ranking of the C and D vines which allow for conditional
sampling, under the condition discussed in the descriprion of CDVineCondFit
.
CDVineCondRank(data, Nx, treecrit = "AIC", selectioncrit = "AIC",
familyset = NA, type = "CVineDVine", indeptest = FALSE, level = 0.05,
se = FALSE, rotations = TRUE, method = "mle")
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 paircopula selection.
Possible choices are 
familyset 
"Integer vector of paircopula families to select from. The vector has to include at least one
paircopula 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 DVine).
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 $par2
. Details about RVineMatrix
objects are given in
the documentation file of the VineCopula
package).
The fit of each model is performed via the function RVineCopSelect
of the package VineCopula
.
"The object is augmented by the following information about the fit:
se, se2
standard errors for the parameter estimates (if se = TRUE
; note that these are only
approximate since they do not account for the sequential nature of the estimation
nobs
number of observations
logLik, pair.logLik
log likelihood (overall and pairwise)
AIC, pair.AIC
Aikaike's Informaton Criterion (overall and pairwise)
BIC, pair.BIC
Bayesian's Informaton Criterion (overall and pairwise)
emptau
matrix of empirical values of Kendall's tau
p.value.indeptest
matrix of pvalues of the independence test.
For a comprehensive summary of the vine copula model, use
summary(object)
; to see all its contents, use str(object)
." (VineCopula Documentation, version 2.1.1, pp. 103)
Emanuele Bevacqua
Bevacqua, E., Maraun, D., Hobaek Haff, I., Widmann, M., and Vrac, M.: Multivariate statistical modelling of compound events via paircopula constructions: analysis of floods in Ravenna (Italy), Hydrol. Earth Syst. Sci., 21, 27012723, https://doi.org/10.5194/hess2127012017, 2017. [link] [link]
Aas, K., Czado, C., Frigessi, A. and Bakken, H.: Paircopula constructions of multiple dependence, Insurance: Mathematics and Economics, 44(2), 182198, <doi:10.1016/j.insmatheco.2007.02.001>, 2009. [link]
Ulf Schepsmeier, Jakob Stoeber, Eike Christian Brechmann, Benedikt Graeler, Thomas Nagler and Tobias Erhardt (2017). VineCopula: Statistical Inference of Vine Copulas. R package version 2.1.1. [link]
# Read data
data(dataset)
data < dataset$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 DVines 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)