Stationary vine copula models — svinecop • svines

Automated fitting or creation of custom S-vine copula models

Usage

svinecop(
  data,
  p,
  var_types,
  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
)

Arguments

data: a matrix or data.frame of pseudo-observations (approximately uniform margins). For discrete variables declared in var_types, append one left-limit column F(x-) per discrete variable after all regular columns. Use svine() to handle this transformation automatically.
p: the Markov order.
var_types: variable types; a character vector with one entry per variable: "c" for continuous, "d" for discrete. Omitting var_types when cs_structure is not provided will silently fit a continuous model.
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: a permutation of 1, ..., d specifying the out-vertices. It is selected automatically when no structure is provided. A fixed cs_structure requires explicit out_vertices and in_vertices.
in_vertices: a permutation of 1, ..., d specifying the in-vertices. It is selected automatically when no structure is provided. A fixed cs_structure requires explicit out_vertices and in_vertices.
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", or "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: the truncation level. Only Inf, corresponding to no truncation, is currently supported.
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.

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.

Examples

# 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 
#> 3-dimensional S-vine copula model of order p = 1 ('svinecop_dist')
summary(fit)
#> # A data.frame: 12 x 10 
#>  tree edge conditioned conditioning var_types   family rotation
#>     1    1        6, 3                    c,c gaussian        0
#>     1    2        1, 2                    c,c     tawn      180
#>     1    3        2, 3                    c,c     tawn      180
#>     2    1        5, 3            6       c,c    indep        0
#>     2    2        6, 2            3       c,c    indep        0
#>     2    3        1, 3            2       c,c    frank        0
#>     3    1        4, 3         6, 5       c,c    indep        0
#>     3    2        5, 2         3, 6       c,c    indep        0
#>     3    3        6, 1         2, 3       c,c    indep        0
#>     4    1        4, 2      3, 6, 5       c,c      joe      180
#>        parameters df    tau
#>             -0.24  1 -0.152
#>  0.79, 1.00, 2.54  3  0.512
#>  0.90, 0.88, 2.87  3  0.551
#>                    0  0.000
#>                    0  0.000
#>                 1  1  0.114
#>                    0  0.000
#>                    0  0.000
#>                    0  0.000
#>               1.1  1  0.046
#> # ... with 2 more rows
plot(fit)

contour(fit)

logLik(fit)
#> [1] 91.58517
#> attr(,"df")
#> [1] 11

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