Description Usage Arguments Details Value References See Also Examples
Estimate a vine model from multivariate data in the unit hypercube. Data can be pseudoobservations constructed from empirical or parametric marginal cumulative distribution functions.
1 
type 
Type of vine. Supported values: 
data 
Data matrix of pseudoobservations. 
method 
Inference method. Supported values:

... 
Additional arguments for the inference method. 
The "ml"
(Maximum Likelihood) method starts with the sequential estimation
procedure described in (Aas et al., 2009) and then executes a numerical
maximization of the full loglikelihood of the vine. The sequential procedure
is used to determine the family and the initial values of the parameters of
each bivariate copula in the decomposition. Additional arguments for this
method are:
selectCopula
Function provided by the user to select the
copulas in the paircopula construction. This function should return
a copula
object and expect the following arguments.
vine
Vine object being constructed.
j, i
Indexes of the copula under selection in the matrix of the
copulas
slot of the vine.
x, y
Bivariate sample.
The default value is function (vine, j, i, x, y) indepCopula()
that assigns the independence copula to all the arcs of the vine.
trees
Maximum number of dependence trees of the vine. Independence
copulas will be used in all the arcs of the following trees. The final number
of dependence trees could be smaller because of the use of a truncation method.
The default value is ncol(data)  1
.
truncMethod
Method used to automatically truncate the vine if
enough dependence is captured in a given number of trees. Supported methods
are "AIC"
and "BIC"
. See (Brechmann, 2010; Brechmann et al., 2010)
for information about these methods. The default value is ""
that
means no truncation.
optimMethod
optim
method used in the optimization
of the loglikelihood function. If ""
is specified the optimization
is disabled and the vine calculated using the sequential estimation procedure
is returned. The default value is "NelderMead"
.
optimControl
List of control parameters for optim
.
The default value is list()
.
A vineFit
object or a subclass with specific
information about inference method used. The vine
slot of this object
contains the fitted Vine
object.
Aas, K. and Czado, C. and Frigessi, A. and Bakken, H. (2009) Paircopula constructions of multiple dependence. Insurance: Mathematics and Economics 44, 182–198.
Brechmann, E. C. (2010) Truncated and simplified regular vines and their applications. Diploma thesis. Technische Universitaet Muenchen.
Brechmann, E. C. and Czado, C. and Aas, K. (2010) Truncated regular vines in high dimensions with application to financial data. Norwegian Computing Center, NR. Note SAMBA/60/10.
CVine
,
DVine
,
vineFit
,
vineFitML
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14  data < matrix(runif(5 * 100), ncol = 5, nrow = 100)
colnames(data) < c("A", "B", "C", "D", "E")
selectCopula < function (vine, j, i, x, y) {
data < cbind(x, y)
fit < fitCopula(normalCopula(), data, method = "itau")
fit@copula
}
fit < vineFit("DVine", data, method = "ml",
selectCopula = selectCopula,
optimMethod = "")
show(fit)
show(fit@vine)

Loading required package: copula
Vine Inference
Method: ml
Vine type: Dvine
Dimension: 5
Observations: 100
Optimization method:
Convergence code: 0
Vine
Type: Dvine
Dimension: 5
Dependency trees: 4
A,B: <deprecated slot> (rho.1 = 0.100740584026723)
B,C: <deprecated slot> (rho.1 = 0.103897253837734)
C,D: <deprecated slot> (rho.1 = 0.0520190559151636)
D,E: <deprecated slot> (rho.1 = 0.0260183360543773)
A,CB: <deprecated slot> (rho.1 = 0.0374364957639043)
B,DC: <deprecated slot> (rho.1 = 0.0298248401712931)
C,ED: <deprecated slot> (rho.1 = 0.0228459587429497)
A,DB,C: <deprecated slot> (rho.1 = 0.167394490441731)
B,EC,D: <deprecated slot> (rho.1 = 0.219072212989425)
A,EB,C,D: <deprecated slot> (rho.1 = 0.112099619611967)
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