Specify which vine copula models are
fitted and how often they are refit as well as how big the training data
set is. Remember that the estimation process is done in a rolling window
fashion and the arguments (train and refit size) will have to match with
the arguments of the also to be specified marginal_settings
.
Usage
vine_settings(train_size, refit_size, family_set = "all", vine_type = "rvine")
# S4 method for vine_settings
show(object)
Arguments
- train_size
equivalent to the slot definition below
- refit_size
equivalent to the slot definition below
- family_set
equivalent to the slot definition below
- vine_type
equivalent to the slot definition below
- object
An object of class
vine_settings
Slots
train_size
Positive count specifying the training data size.
refit_size
Positive count specifying for how many periods a vine is used
family_set
Character vector specifying the family of copulas that are used. For possible choices see
rvinecopulib::bicop
. Note for conditional sampling just parametric copula families are possible so do not use the family argumentsall
andtll
.vine_type
character value that specifies which vine class should be fitted. Possible choices right now are
rvine
(regular vine) anddvine
(drawable vine).
Examples
# the most basic initialization
vine_settings(100, 25)
#> An object of class <vine_settings>
#> train_size: 100
#> refit_size: 25
#> family_set: all
#> vine_type: rvine
# some individual note
vine_settings(
train_size = 100, refit_size = 20,
family_set = c("gumbel", "joe"),
vine_type = "dvine"
)
#> An object of class <vine_settings>
#> train_size: 100
#> refit_size: 20
#> family_set: gumbel joe
#> vine_type: dvine