vine {rvinecopulib} | R Documentation |
Vine copula models
Description
Automated fitting or creation of custom vine copula models
Usage
vine(
data,
margins_controls = list(mult = NULL, xmin = NaN, xmax = NaN, bw = NA, deg = 2),
copula_controls = list(family_set = "all", structure = NA, par_method = "mle",
nonpar_method = "constant", mult = 1, selcrit = "aic", psi0 = 0.9, presel = TRUE,
trunc_lvl = Inf, tree_crit = "tau", threshold = 0, keep_data = FALSE, show_trace =
FALSE, cores = 1),
weights = numeric(),
keep_data = FALSE,
cores = 1
)
vine_dist(margins, pair_copulas, structure)
Arguments
data |
a matrix or data.frame. Discrete variables have to be declared as
|
margins_controls |
a list with arguments to be passed to
|
copula_controls |
a list with arguments to be passed to |
weights |
optional vector of weights for each observation. |
keep_data |
whether the original data should be stored; if you want to
store the pseudo-observations used for fitting the copula, use the
|
cores |
the number of cores to use for parallel computations. |
margins |
A list with with each element containing the specification of a
marginal stats::Distributions. Each marginal specification
should be a list with containing at least the distribution family ( |
pair_copulas |
A nested list of 'bicop_dist' objects, where
|
structure |
an |
Details
vine_dist()
creates a vine copula by specifying the margins, a nested list
of bicop_dist
objects and a quadratic structure matrix.
vine()
provides automated fitting for vine copula models.
margins_controls
is a list with the same parameters as
kde1d::kde1d()
(except for x
). copula_controls
is a list
with the same parameters as vinecop()
(except for data
).
Value
Objects inheriting from vine_dist
for vine_dist()
, and
vine
and vine_dist
for vine()
.
Objects from the vine_dist
class are lists containing:
-
margins
, a list of marginals (see below). -
copula
, an object of the classvinecop_dist
, seevinecop_dist()
.
For objects from the vine
class, copula
is also an object of the class
vine
, see vinecop()
. Additionally, objects from the vine
class contain:
-
margins_controls
, alist
with the set of fit controls that was passed tokde1d::kde1d()
when estimating the margins. -
copula_controls
, alist
with the set of fit controls that was passed tovinecop()
when estimating the copula. -
data
(optionally, ifkeep_data = TRUE
was used), the dataset that was passed tovine()
. -
nobs
, aninteger
containing the number of observations that was used to fit the model.
Concerning margins
:
For objects created with
vine_dist()
, it simply corresponds to themargins
argument.For objects created with
vine()
, it is a list of objects of classkde1d
, seekde1d::kde1d()
.
Examples
# specify pair-copulas
bicop <- bicop_dist("bb1", 90, c(3, 2))
pcs <- list(
list(bicop, bicop), # pair-copulas in first tree
list(bicop) # pair-copulas in second tree
)
# specify R-vine matrix
mat <- matrix(c(1, 2, 3, 1, 2, 0, 1, 0, 0), 3, 3)
# set up vine copula model with Gaussian margins
vc <- vine_dist(list(distr = "norm"), pcs, mat)
# show model
summary(vc)
# simulate some data
x <- rvine(50, vc)
# estimate a vine copula model
fit <- vine(x, copula_controls = list(family_set = "par"))
summary(fit)
## model for discrete data
x <- as.data.frame(x)
x[, 1] <- ordered(round(x[, 1]), levels = seq.int(-5, 5))
fit_disc <- vine(x, copula_controls = list(family_set = "par"))
summary(fit_disc)