svinecop: Stationary vine copula models

Description

Automated fitting or creation of custom S-vine copula models

Usage

svinecop(
  data,
  p,
  var_types = rep("c", NCOL(data)),
  family_set = "all",
  cs_structure = NA,
  out_vertices = NA,
  in_vertices = NA,
  type = "S",
  par_method = "mle",
  nonpar_method = "constant",
  mult = 1,
  selcrit = "aic",
  weights = numeric(),
  psi0 = 0.9,
  presel = TRUE,
  trunc_lvl = Inf,
  tree_crit = "tau",
  threshold = 0,
  keep_data = FALSE,
  show_trace = FALSE,
  cores = 1
)

Value

Returns the fitted model as an object with classes svinecop and svinecop_dist. Also inherits from vinecop, vinecop_dist

such that many functions from rvinecopulib can be called.

Arguments

data: a matrix or data.frame (copula data should have approximately uniform margins).
p: the Markov order.
var_types: variable types; discrete variables not (yet) allowed.
family_set: a character vector of families; see rvinecopulib::bicop() for additional options.
cs_structure: the cross-sectional vine structure (see rvinecopulib::rvine_structure(); cs_structure = NA performs automatic structure selection.
out_vertices: the out-vertex; if NA, the out-vertex is selected automatically if no structure is provided, and is equivalent to 1 if a structure is provided.
in_vertices: the in-vertex; if NA, the in-vertex is selected automatically if no structure is provided, and is equivalent to 1 if a structure is provided.
type: type of stationary vine; "S" (default) for general S-vines, "D" for Smith's long D-vine, "M" for Beare and Seo's M-vine.
par_method: the estimation method for parametric models, either "mle" for sequential maximum likelihood, "itau" for inversion of Kendall's tau (only available for one-parameter families and "t".
nonpar_method: the estimation method for nonparametric models, either "constant" for the standard transformation estimator, or "linear"/"quadratic" for the local-likelihood approximations of order one/two.
mult: multiplier for the smoothing parameters of nonparametric families. Values larger than 1 make the estimate more smooth, values less than 1 less smooth.
selcrit: criterion for family selection, either "loglik", "aic", "bic", "mbic". For vinecop() there is the additional option "mbicv".
weights: optional vector of weights for each observation.
psi0: prior probability of a non-independence copula (only used for selcrit = "mbic" and selcrit = "mbicv").
presel: whether the family set should be thinned out according to symmetry characteristics of the data.
trunc_lvl: currently unsupported.
tree_crit: the criterion for tree selection, one of "tau", "rho", "hoeffd", or "mcor" for Kendall's \(\tau\), Spearman's \(\rho\), Hoeffding's \(D\), and maximum correlation, respectively.
threshold: for thresholded vine copulas; NA indicates that the threshold should be selected automatically by rvinecopulib::mBICV().
keep_data: whether the data should be stored (necessary for using fitted()).
show_trace: logical; whether a trace of the fitting progress should be printed.
cores: number of cores to use; if more than 1, estimation of pair copulas within a tree is done in parallel.

Examples

Run this code

# load data set
data(returns)  

# convert to pseudo observations with empirical cdf for marginal distributions
u <- pseudo_obs(returns[1:100, 1:3]) 

# fit parametric S-vine copula model with Markov order 1
fit <- svinecop(u, p = 1, family_set = "parametric")
fit 
summary(fit)
plot(fit)
contour(fit)
logLik(fit)

pairs(svinecop_sim(500, rep = 1, fit))

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