Package 'rvinecopulib'

Description

as_margin() validates fitted margins and adapts the legacy list(distr = ...) representation through stats_margin().

Usage

as_margin(margin)

Arguments

Value

A validated fitted margin.

Description

as_rvine_structure and as_rvine_matrix are new S3 generics allowing to coerce objects into R-vine structures and matrices (see rvine_structure() and rvine_matrix()).

Usage

as_rvine_structure(x, ...)

as_rvine_matrix(x, ...)

## S3 method for class 'rvine_structure'
as_rvine_structure(x, ..., validate = FALSE)

## S3 method for class 'rvine_structure'
as_rvine_matrix(x, ..., validate = FALSE)

## S3 method for class 'list'
as_rvine_structure(x, ..., is_natural_order = FALSE)

## S3 method for class 'list'
as_rvine_matrix(x, ..., is_natural_order = FALSE)

## S3 method for class 'rvine_matrix'
as_rvine_structure(x, ..., validate = FALSE)

## S3 method for class 'rvine_matrix'
as_rvine_matrix(x, ..., validate = FALSE)

## S3 method for class 'matrix'
as_rvine_structure(x, ..., validate = TRUE)

## S3 method for class 'matrix'
as_rvine_matrix(x, ..., validate = TRUE)

Arguments

Details

The coercion to rvine_structure and rvine_matrix can be applied to different kind of objects Currently, rvine_structure, rvine_matrix, matrix and list are supported.

For as_rvine_structure:

• rvine_structure : the main use case is to re-check an object via validate = TRUE.

• rvine_matrix and matrix : allow to coerce matrices into R-vine structures (see rvine_structure() for more details). The main difference between rvine_matrix and matrix is the nature of the validity checks.

• list : must contain named elements order and struct_array to be coerced into an R-vine structure (see rvine_structure() for more details).

For as_rvine_matrix:

• rvine_structure : allow to coerce an rvine_structure into an R-vine matrix (useful e.g. for printing).

• rvine_matrix: similar to as_rvine_structure for rvine_structure, the main use case is to re-check an object via validate = TRUE.

• matrix : allow to coerce matrices into R-vine matrices (mainly by checking that the matrix defines a valid R-vine, see rvine_matrix() for more details).

• list : must contain named elements order and struct_array to be coerced into an R-vine matrix (see rvine_structure() for more details).

Value

Either an object of class rvine_structure or of class rvine_matrix (see rvine_structure() or rvine_matrix()).

See Also

rvine_structure rvine_matrix

Examples

# R-vine structures can be constructed from the order vector and struct_array
rvine_structure(order = 1:4, struct_array = list(
  c(4, 4, 4),
  c(3, 3),
  2
))

# ... or a similar list can be coerced into an R-vine structure
as_rvine_structure(list(order = 1:4, struct_array = list(
  c(4, 4, 4),
  c(3, 3),
  2
)))

# similarly, standard matrices can be coerced into R-vine structures
mat <- matrix(c(4, 3, 2, 1, 4, 3, 2, 0, 4, 3, 0, 0, 4, 0, 0, 0), 4, 4)
as_rvine_structure(mat)

# or truncate and construct the structure
mat[3, 1] <- 0
as_rvine_structure(mat)

# throws an error
mat[3, 1] <- 5
try(as_rvine_structure(mat))

Description

Convert list to bicop object

Usage

as.bicop(object, check = TRUE)

Arguments

Value

A bicop object corresponding to the specification in object.

Examples

as.bicop(list(family = "gumbel", rotation = 90, parameters = 2, npars = 1))

Description

Fit a bivariate copula model for continuous or discrete data. The family can be selected automatically from a vector of options.

Usage

bicop(
  data,
  var_types = c("c", "c"),
  family_set = "all",
  par_method = "mle",
  nonpar_method = "quadratic",
  mult = 1,
  selcrit = "aic",
  weights = numeric(),
  psi0 = 0.9,
  presel = TRUE,
  allow_rotations = TRUE,
  keep_data = FALSE,
  cores = 1
)

Arguments

Details

If there are missing data (i.e., NA entries), incomplete observations are discarded before fitting the copula.

Discrete variables

When at least one variable is discrete, more than two columns are required for data: the first $n \times 2$ block contains realizations of $F_{X_1}(x_1), F_{X_2}(x_2)$. The second $n \times 2$ block contains realizations of $F_{X_1}(x_1^-), F_{X_2}(x_2^-)$. The minus indicates a left-sided limit of the cdf. For, e.g., an integer-valued variable, it holds $F_{X_1}(x_1^-) = F_{X_1}(x_1 - 1)$. For continuous variables the left limit and the cdf itself coincide. Respective columns can be omitted in the second block.

Family collections

The family_set argument accepts all families in bicop_dist() plus the following convenience definitions:

• "all" contains all the families,

• "parametric" contains the parametric families (all except "tll"),

• "nonparametric" contains the nonparametric families ("indep" and "tll")

• "onepar" contains the parametric families with a single parameter,

("gaussian", "clayton", "gumbel", "frank", and "joe"),

• "twopar" contains the parametric families with two parameters, ("t", "bb1", "bb6", "bb7", and "bb8"),

• "threepar" contains the parametric families with three parameters, ("tawn"),

• "elliptical" contains the elliptical families,

• "archimedean" contains the archimedean families,

• "ev" contains the extreme-value families,

• "BB" contains the BB families,

• "itau" families for which estimation by Kendall's tau inversion is available ("indep","gaussian", "t","clayton", "gumbel", "frank", "joe").

Value

An object inheriting from classes bicop and bicop_dist . In addition to the entries contained in bicop_dist(), objects from the bicop class contain:

• data (optionally, if keep_data = TRUE was used), the dataset that was passed to bicop().

• controls, a list with the set of fit controls that was passed to bicop().

• loglik the log-likelihood.

• nobs, an integer with the number of observations that was used to fit the model.

See Also

bicop_dist(), plot.bicop(), contour.bicop(), dbicop(), pbicop(), hbicop(), rbicop()

Examples

## fitting a continuous model from simulated data
u <- rbicop(100, "clayton", 90, 3)
fit <- bicop(u, family_set = "par")
summary(fit)

## compare fit with true model
contour(fit)
contour(bicop_dist("clayton", 90, 3), col = 2, add = TRUE)

## fit a model from discrete data
x_disc <- qpois(u, 1)  # transform to Poisson margins
plot(x_disc)
udisc <- cbind(ppois(x_disc, 1), ppois(x_disc - 1, 1))
fit_disc <- bicop(udisc, var_types = c("d", "d"))
summary(fit_disc)

Description

Computes the four corner tail-dependence coefficients or Blomqvist's beta for a bivariate copula distribution.

Usage

tail_dep(object)

blomqvist_beta(object)

Arguments

Value

tail_dep() returns a 2 by 2 matrix whose rows refer to the lower and upper tail of the first variable and whose columns refer to the lower and upper tail of the second variable. blomqvist_beta() returns a numeric scalar.

Examples

cop <- bicop_dist("clayton", 0, 2)
tail_dep(cop)
blomqvist_beta(cop)

Description

Create custom bivariate copula models by specifying the family, rotation, parameters, and variable types.

Usage

bicop_dist(
  family = "indep",
  rotation = 0,
  parameters = numeric(0),
  var_types = c("c", "c")
)

Arguments

Details

Implemented families

Value

An object of class bicop_dist, i.e., a list containing:

• family, a character indicating the copula family.

• rotation, an integer indicating the rotation (i.e., either 0, 90, 180, or 270).

• parameters, a numeric vector or matrix of parameters.

• npars, a numeric with the (effective) number of parameters.

• var_types, the variable types.

See Also

bicop_dist(), plot.bicop(), contour.bicop(), dbicop(), pbicop(), hbicop(), rbicop()

Examples

## Clayton 90° copula with parameter 3
cop <- bicop_dist("clayton", 90, 3)
cop
str(cop)

## visualization
plot(cop)
contour(cop)
plot(rbicop(200, cop))

## BB8 copula model for discrete data
cop_disc <- bicop_dist("bb8", 0, c(2, 0.5), var_types = c("d", "d"))
cop_disc

Description

Density, distribution function, random generation and h-functions (with their inverses) for the bivariate copula distribution.

Usage

dbicop(
  u,
  family,
  rotation,
  parameters,
  var_types = c("c", "c"),
  log = FALSE,
  deriv = NULL,
  cores = 1
)

pbicop(u, family, rotation, parameters, var_types = c("c", "c"))

rbicop(n, family, rotation, parameters, qrng = FALSE)

## S3 method for class 'bicop_dist'
scores(u, vinecop, cores = 1, parameters = NULL, ...)

## S3 method for class 'bicop_dist'
hessian(u, vinecop, cores = 1, parameters = NULL, ...)

hbicop(
  u,
  cond_var,
  family,
  rotation,
  parameters,
  inverse = FALSE,
  var_types = c("c", "c"),
  deriv = NULL,
  cores = 1
)

Arguments

Details

See bicop for the various implemented copula families.

The copula density is defined as joint density divided by marginal densities, irrespective of variable types.

H-functions (hbicop()) are conditional distributions derived from a copula. If $C(u, v) = P(U \le u, V \le v)$ is a copula, then

$h_1(u, v) = P(V \le v | U = u) = \partial C(u, v) / \partial u,$

$h_2(u, v) = P(U \le u | V = v) = \partial C(u, v) / \partial v.$

In other words, the H-function number refers to the conditioning variable. When inverting H-functions, the inverse is then taken with respect to the other variable, that is v when cond_var = 1 and u when cond_var = 2.

Derivatives

Setting deriv evaluates a selected first- or second-order derivative of the density, log-density, or h-function. For example, deriv = "u1" differentiates with respect to the first argument and deriv = c("u1", "par2") evaluates the corresponding mixed second derivative. Derivative order is immaterial. Parameter derivatives use the natural parameters of the (possibly rotated) copula.

Derivatives are available only for continuous parametric copulas. The backend uses analytic formulas where available and finite-difference fallbacks where needed. Derivatives of inverse h-functions are not available.

Discrete variables

When at least one variable is discrete, more than two columns are required for u: the first $n \times 2$ block contains realizations of $F_{X_1}(x_1), F_{X_2}(x_2)$. The second $n \times 2$ block contains realizations of $F_{X_1}(x_1^-), F_{X_2}(x_2^-)$. The minus indicates a left-sided limit of the cdf. For, e.g., an integer-valued variable, it holds $F_{X_1}(x_1^-) = F_{X_1}(x_1 - 1)$. For continuous variables the left limit and the cdf itself coincide. Respective columns can be omitted in the second block.

Value

dbicop() gives the density or log-density, and pbicop() gives the distribution function. rbicop() generates random deviates, and hbicop() gives the h-functions (and their inverses). If deriv is set, dbicop() and hbicop() return the selected observation-wise derivative. scores() gives the observation-wise score matrix and hessian() gives the average Hessian matrix for a bicop_dist object.

The length of the result is determined by n for rbicop(), and the number of rows in u for the other functions.

The numerical arguments other than n are recycled to the length of the result. For vectorized simulation, parameters must have one row per generated observation and one column per family parameter.

Note

The functions can optionally be used with a bicop_dist object in place of the family argument, e.g., dbicop(c(0.1, 0.5), bicop_dist("indep")) or hbicop(c(0.1, 0.5), 2, bicop_dist("indep")).

See Also

bicop_dist(), bicop()

Examples

## evaluate the copula density
dbicop(c(0.1, 0.2), "clay", 90, 3)
dbicop(c(0.1, 0.2), bicop_dist("clay", 90, 3))

## evaluate the copula cdf
pbicop(c(0.1, 0.2), "clay", 90, 3)

## simulate data
plot(rbicop(500, "clay", 90, 3))

## h-functions
joe_cop <- bicop_dist("joe", 0, 3)
# h_1(0.1, 0.2)
hbicop(c(0.1, 0.2), 1, "bb8", 0, c(2, 0.5))
# h_2^{-1}(0.1, 0.2)
hbicop(c(0.1, 0.2), 2, joe_cop, inverse = TRUE)

## derivatives
dbicop(c(0.2, 0.4), joe_cop, deriv = "u1")
dbicop(c(0.2, 0.4), joe_cop, log = TRUE, deriv = "par1")
hbicop(c(0.2, 0.4), 1, joe_cop, deriv = c("u1", "par1"))

## mixed discrete and continuous data
x <- cbind(rpois(10, 1), rnorm(10, 1))
u <- cbind(ppois(x[, 1], 1), pnorm(x[, 2]), ppois(x[, 1] - 1, 1))
pbicop(u, "clay", 90, 3, var_types = c("d", "c"))

Description

Predictions of the density, distribution function, h-functions (with their inverses) for a bivariate copula model.

Usage

## S3 method for class 'bicop_dist'
predict(object, newdata, what = "pdf", ...)

## S3 method for class 'bicop'
fitted(object, what = "pdf", ...)

Arguments

Details

fitted() can only be called if the model was fit with the keep_data = TRUE option.

Discrete variables

When at least one variable is discrete, more than two columns are required for newdata: the first $n \times 2$ block contains realizations of $F_{X_1}(x_1), F_{X_2}(x_2)$. The second $n \times 2$ block contains realizations of $F_{X_1}(x_1^-), F_{X_2}(x_2^-)$. The minus indicates a left-sided limit of the cdf. For, e.g., an integer-valued variable, it holds $F_{X_1}(x_1^-) = F_{X_1}(x_1 - 1)$. For continuous variables the left limit and the cdf itself coincide. Respective columns can be omitted in the second block.

Value

fitted() and logLik() have return values similar to dbicop(), pbicop(), and hbicop().

Examples

# Simulate and fit a bivariate copula model
u <- rbicop(500, "gauss", 0, 0.5)
fit <- bicop(u, family_set = "par", keep_data = TRUE)

# Predictions
all.equal(predict(fit, u, "hfunc1"), fitted(fit, "hfunc1"),
          check.environment = FALSE)

Description

The empirical CDF with tail correction, ensuring that its output is never 0 or 1.

Usage

emp_cdf(x)

Arguments

Details

The corrected empirical CDF is defined as

$F_n(x) = \frac{1}{n + 1} \max\biggl\{1, \sum_{i = 1}^n 1(X_i \le x)\biggr\}$

Value

A function with signature ⁠function(x)⁠ that returns $F_n(x)$.

Examples

# fit ECDF on simulated data
x <- rnorm(100)
cdf <- emp_cdf(x)

# output is bounded away from 0 and 1
cdf(-50)
cdf(50)

Description

Extracts either the structure matrix (for vinecop_dist only), or pair-copulas, their parameters, Kendall's taus, or families (for bicop_dist and vinecop_dist).

Usage

get_structure(object)

get_pair_copula(object, tree = NA, edge = NA)

get_parameters(object, tree = NA, edge = NA)

get_ktau(object, tree = NA, edge = NA)

get_family(object, tree = NA, edge = NA)

get_all_pair_copulas(object, trees = NA)

get_all_parameters(object, trees = NA)

get_all_ktaus(object, trees = NA)

get_all_families(object, trees = NA)

Arguments

Details

#' The get_structure method (for vinecop_dist or vine_dist objects only) extracts the structure (see rvine_structure for more details).

The get_matrix method (for vinecop_dist or vine_dist objects only) extracts the structure matrix (see rvine_structure for more details).

The other get_xyz methods for vinecop_dist or vine_dist objects return the entries corresponding to the pair-copula indexed by its tree and edge. When object is of class bicop_dist, tree and edge are not required.

• get_pair_copula() = the pair-copula itself (see bicop).

• get_parameters() = the parameters of the pair-copula (i.e., a numeric scalar, vector, or matrix).

• get_family() = a character for the family (see bicop for implemented families).

• get_ktau() = a numeric scalar with the pair-copula Kendall's tau.

The get_all_xyz methods (for vinecop_dist or vine_dist objects only) return lists of lists, with each element corresponding to a tree in trees, and then elements of the sublists correspond to edges. The returned lists have two additional attributes:

• "d" = the dimension of the model.

• "trees" = the extracted trees.

Value

The structure matrix, or pair-copulas, their parameters, Kendall's taus, or families.

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
vc <- vinecop_dist(pcs, mat)

# get the structure
get_structure(vc)
all(get_matrix(vc) == mat)

# get pair-copulas
get_pair_copula(vc, 1, 1)
get_all_pair_copulas(vc)
all.equal(get_all_pair_copulas(vc), pcs,
          check.attributes = FALSE, check.environment = FALSE)

Description

The arguments configure kde1d::kde1d() and travel with the family object, rather than being interpreted by vine().

Usage

kde1d_family(xmin = NaN, xmax = NaN, mult = 1, bw = NA, deg = 2)

Arguments

Value

A margin-family specification supporting all rvinecopulib variable types.

Description

margin_dist() creates a fitted or fixed margin from evaluation functions and explicit metadata. The functions must each accept one vector argument and return a numeric vector of the same length.

Usage

margin_dist(
  d,
  p,
  q,
  family = "custom",
  type = "c",
  support = c(-Inf, Inf),
  npars = 0,
  loglik = NA_real_
)

Arguments

Value

A fitted margin implementing the margin protocol.

Examples

normal_margin <- margin_dist(
  d = function(x) dnorm(x, mean = 1, sd = 2),
  p = function(x) pnorm(x, mean = 1, sd = 2),
  q = function(p) qnorm(p, mean = 1, sd = 2),
  family = "norm",
  support = c(-Inf, Inf),
  npars = 2
)
dmargin(1, normal_margin)
pmargin(1, normal_margin)
qmargin(0.5, normal_margin)

Description

margin_family() defines a candidate family for vine(). Its fitting function must accept x, weights, and type, and return an object implementing the fitted-margin protocol. Additional named fitting arguments can be stored in fit_args.

Usage

margin_family(fit, family_name = "custom", types = "c", fit_args = list())

Arguments

Value

A margin-family specification.

Examples

normal_family <- margin_family(
  fit = function(x, weights, type) {
    if (length(weights)) {
      location <- weighted.mean(x, weights)
      scale <- sqrt(weighted.mean((x - location)^2, weights))
    } else {
      location <- mean(x)
      scale <- sd(x)
    }
    margin_dist(
      d = function(y) dnorm(y, location, scale),
      p = function(y) pnorm(y, location, scale),
      q = function(p) qnorm(p, location, scale),
      family = "normal",
      type = type,
      npars = 2,
      loglik = sum(dnorm(x, location, scale, log = TRUE))
    )
  },
  family_name = "normal"
)

Description

Margin families describe how a candidate is fitted. Implementations provide fit_margin() and margin_info() methods. Their margin_info() result must contain family_name and types. Every fitter receives the observations, observation weights, and requested variable type. It must either use the weights or reject them.

Usage

fit_margin(family, x, weights = numeric(), type = "c")

Arguments

Value

fit_margin() returns a fitted margin.

Description

Fitted margins provide distribution evaluation and model metadata through a small set of S3 generics. Methods for dmargin(), pmargin(), and qmargin() dispatch on margin, their second argument. margin_info() dispatches on its only argument.

Usage

dmargin(x, margin)

pmargin(x, margin)

qmargin(p, margin)

margin_info(object)

Arguments

Details

A fitted margin class must implement all four generics documented here. Evaluation methods must be vectorized. margin_info() returns a list with entries family_name, type, support, npars, and loglik. type is "c" for a continuous distribution, "d" for an integer-supported distribution, or "zi" for a continuous distribution with an atom at zero. support contains the mathematical lower and upper bounds. npars must be non-negative or NA when unavailable; loglik may be NA for a fixed margin.

The type determines left limits throughout rvinecopulib. They are F(x) for continuous margins, F(x - 1) for integer-supported margins, and F(0) - P(X = 0) at zero for zero-inflated margins. Consequently, dmargin(0, margin) must return the atom's mass for a zero-inflated margin.

Value

dmargin(), pmargin(), and qmargin() return numeric vectors. margin_info() returns a list of model information.

Description

Calculates the modified vine copula Bayesian information criterion.

Usage

mBICV(object, psi0 = 0.9, newdata = NULL)

Arguments

Details

The modified vine copula Bayesian information criterion (mBICv) is defined as

$BIC = -2 loglik + \nu log(n) - 2 \sum_{t=1}^{d - 1} (q_t log(\psi_0^t) + (d - t - q_t) log(1 - \psi_0^t))$

where $\mathrm{loglik}$ is the log-likelihood and $\nu$ is the (effective) number of parameters of the model, $t$ is the tree level $\psi_0$ is the prior probability of having a non-independence copula and $q_t$ is the number of non-independence copulas in tree $t$. The mBICv is a consistent model selection criterion for parametric sparse vine copula models.

References

Nagler, T., Bumann, C., Czado, C. (2019). Model selection for sparse high-dimensional vine copulas with application to portfolio risk. Journal of Multivariate Analysis, in press (https://arxiv.org/pdf/1801.09739)

Examples

u <- sapply(1:5, function(i) runif(50))
fit <- vinecop(u, family_set = "par", keep_data = TRUE)
mBICV(fit, 0.9) # with a 0.9 prior probability of a non-independence copula
mBICV(fit, 0.1) # with a 0.1 prior probability of a non-independence copula

Description

This function provides pair plots for copula data. It shows bivariate contour plots on the lower panel, scatter plots and correlations on the upper panel and histograms on the diagonal panel.

Usage

pairs_copula_data(data, main = "", ...)

Arguments

Examples

u <- replicate(3, runif(100))
pairs_copula_data(u)

Description

Conversion between Kendall's tau and parameters

Usage

par_to_ktau(family, rotation, parameters)

ktau_to_par(family, tau)

Arguments

Examples

# the following are equivalent
par_to_ktau(bicop_dist("clayton", 0, 3))
par_to_ktau("clayton", 0, 3)

ktau_to_par("clayton", 0.5)
ktau_to_par(bicop_dist("clayton", 0, 3), 0.5)

Description

Covariance matrices and Wald confidence intervals for the estimated pair-copula parameters.

Usage

## S3 method for class 'vinecop_dist'
vcov(object, newdata = NULL, step_wise = TRUE, cores = 1, ...)

## S3 method for class 'bicop_dist'
vcov(object, newdata = NULL, step_wise = TRUE, cores = 1, ...)

## S3 method for class 'vinecop_dist'
confint(object, parm, level = 0.95, ...)

## S3 method for class 'bicop_dist'
confint(object, parm, level = 0.95, ...)

Arguments

Details

The estimator solves $\bar s(\hat\theta) = 0$ with $\bar s(\theta) = n^{-1} \sum_i s_i(\theta)$, so it is an M-estimator. Writing

$A = -\partial \bar s(\theta) / \partial \theta^\top, \qquad B = n^{-1} \sum_i s_i s_i^\top,$

vcov() returns the sandwich $A^{-1} B A^{-\top} / n$ of White (1982).

Which objective is differentiated

step_wise selects the estimating equation, and it changes the nature of $A$.

With step_wise = FALSE, $\bar s$ is the gradient of the joint log-likelihood and $A$ is minus its averaged Hessian: a symmetric matrix, and the classical setting for both formulas.

With step_wise = TRUE (the default), the pseudo-observations entering each pair copula are held fixed. This is the estimating equation that sequential, tree-by-tree estimation actually solves, but it is not the gradient of any objective: $A$ is a Jacobian rather than a Hessian, and it is block triangular rather than symmetric, which is why the sandwich is written with $A^{-\top}$ on the right.

Restrictions

No derivatives are implemented for nonparametric pair copulas, so a model containing one is refused. For models with discrete variables the backend differentiates by central finite differences rather than in closed form; the result is correct but carries the accuracy of a finite-difference approximation.

Why there is no method for vine distributions

These functions treat the copula data as observed, so they quantify uncertainty in the pair-copula parameters given the margins. A model fitted by vine() estimates its margins too, and the second stage inherits the first stage's sampling variability: intervals that ignore it are too short. In a small experiment (three-dimensional Gaussian D-vine, equicorrelation 0.5, n = 1000, 1000 replications) nominal 95% intervals attained 0.956 when the true margins were used and 0.929 when they were estimated by ranks.

Correcting for it needs the influence function of the marginal estimator, and fit_margin() is a user-supplied black box, so there is nothing to differentiate. We therefore provide no method for "vine" objects rather than a silently anticonservative one. To quantify uncertainty for a complete vine distribution, resample:

boot <- replicate(B, {
  idx <- sample(nrow(x), replace = TRUE)
  fit <- vine(x[idx, ], margins_controls = mc, copula_controls = cc)
  unlist(get_all_parameters(fit$copula))
})
apply(boot, 1, quantile, c(0.025, 0.975))

Value

vcov() returns a covariance matrix with one row and column per estimated pair-copula parameter, ordered by tree, then edge, then parameter within the pair copula, with dimnames giving that position. confint() returns a matrix with columns for the lower and upper endpoints.

References

White H (1982). "Maximum Likelihood Estimation of Misspecified Models." Econometrica, 50(1), 1–25. doi:10.2307/1912526

Stoeber J, Schepsmeier U (2013). "Estimating Standard Errors in Regular Vine Copula Models." Computational Statistics, 28(6), 2679–2707. doi:10.1007/s00180-013-0423-8

See Also

scores(), hessian(), vinecop(), vine()

Examples

# bivariate copula ------------------------------------------
u <- rbicop(500, "gaussian", 0, 0.5)
fit <- bicop(u, family_set = "parametric", keep_data = TRUE)
sqrt(diag(vcov(fit)))
confint(fit)

# vine copula -----------------------------------------------
u <- pseudo_obs(matrix(rnorm(500 * 3), 500, 3))
fit <- vinecop(u, family_set = "parametric", keep_data = TRUE)
vcov(fit)
confint(fit, level = 0.9)

# derivatives of the joint log-likelihood instead of the step-wise ones
sqrt(diag(vcov(fit, step_wise = FALSE)))

Description

There are several options for plotting bicop_dist objects. The density of a bivariate copula density can be visualized as surface/perspective or contour plot. Optionally, the density can be coupled with standard normal margins (default for contour plots).

Usage

## S3 method for class 'bicop_dist'
plot(x, type = "surface", margins, size, ...)

## S3 method for class 'bicop'
plot(x, type = "surface", margins, size, ...)

## S3 method for class 'bicop_dist'
contour(x, margins = "norm", size = 100L, ...)

## S3 method for class 'bicop'
contour(x, margins = "norm", size = 100L, ...)

Arguments

See Also

bicop_dist(), graphics::contour(), lattice::wireframe()

Examples

## construct bicop_dist object for a student t copula
obj <- bicop_dist(family = "t", rotation = 0, parameters = c(0.7, 4))

## plots
plot(obj) # surface plot of copula density
contour(obj) # contour plot with standard normal margins
contour(obj, margins = "unif") # contour plot of copula density

Description

Plot one or all trees of an R-vine structure.

Usage

## S3 method for class 'rvine_structure'
plot(x, ...)

## S3 method for class 'rvine_matrix'
plot(x, ...)

Arguments

Examples

plot(cvine_structure(1:5))
plot(rvine_structure_sim(5))
mat <- rbind(c(1, 1, 1), c(2, 2, 0), c(3, 0, 0))
plot(rvine_matrix(mat))
plot(rvine_matrix_sim(5))

Description

There are two plotting generics for vinecop_dist objects. plot.vinecop_dist plots one or all trees of a given R-vine copula model. Edges can be labeled with information about the corresponding pair-copula. contour.vinecop_dist produces a matrix of contour plots (using plot.bicop).

Usage

## S3 method for class 'vinecop_dist'
plot(x, tree = 1, var_names = "ignore", edge_labels = NULL, ...)

## S3 method for class 'vinecop'
plot(x, tree = 1, var_names = "ignore", edge_labels = NULL, ...)

## S3 method for class 'vinecop_dist'
contour(x, tree = "ALL", cex.nums = 1, ...)

## S3 method for class 'vinecop'
contour(x, tree = "ALL", cex.nums = 1, ...)

Arguments

Details

If you want the contour boxes to be perfect squares, the plot height should be 1.25/length(tree)*(d - min(tree)) times the plot width.

The plot() method returns an object that (among other things) contains the igraph representation of the graph; see Examples.

Author(s)

Thomas Nagler, Thibault Vatter

See Also

vinecop_dist, plot.bicop

Examples

# set up vine copula model
u <- matrix(runif(20 * 10), 20, 10)
vc <- vinecop(u, family_set = "indep")

# plot
plot(vc, tree = c(1, 2))
plot(vc, edge_labels = "pair")

# extract igraph representation
plt <- plot(vc, edge_labels = "family_tau")
igr_obj <- get("g", plt$plot_env)[[1]]
igr_obj  # print object
igraph::E(igr_obj)$name  # extract edge labels

# set up another vine copula model
pcs <- lapply(1:3, function(j) # pair-copulas in tree j
  lapply(runif(4 - j), function(cor) bicop_dist("gaussian", 0, cor)))
mat <- rvine_matrix_sim(4)
vc <- vinecop_dist(pcs, mat)

# contour plot
contour(vc)

Description

Compute the pseudo-observations for the given data matrix.

Usage

pseudo_obs(x, ties_method = "average", lower_tail = TRUE)

Arguments

Details

Given n realizations $x_i=(x_{i1}, \ldots,x_{id})$, $i \in \left\lbrace 1, \ldots,n \right\rbrace$ of a random vector X, the pseudo-observations are defined via $u_{ij}=r_{ij}/(n+1)$ for $i \in \left\lbrace 1, \ldots,n \right\rbrace$ and $j \in \left\lbrace 1, \ldots,d \right\rbrace$, where $r_{ij}$ denotes the rank of $x_{ij}$ among all $x_{kj}$, $k \in \left\lbrace 1, \ldots,n \right\rbrace$.

The pseudo-observations can thus also be computed by component-wise applying the empirical distribution functions to the data and scaling the result by $n/(n+1)$. This asymptotically negligible scaling factor is used to force the variates to fall inside the open unit hypercube, for example, to avoid problems with density evaluation at the boundaries.

When lower_tail = FALSE, then pseudo_obs() simply returns 1 - pseudo_obs().

Value

a vector of matrix of the same dimension as the input containing the pseudo-observations.

Examples

# pseudo-observations for a vector
pseudo_obs(rnorm(10))

# pseudo-observations for a matrix
pseudo_obs(cbind(rnorm(10), rnorm(10)))

Description

The Rosenblatt transform maps observations from a model to independent uniform variates. The inverse transform evaluates conditional quantiles and maps independent uniforms to draws from the model; see Details.

Usage

rosenblatt(
  x,
  model,
  cores = 1,
  randomize_discrete = TRUE,
  conditioning_set = NULL
)

inverse_rosenblatt(u, model, cores = 1, conditioning_set = NULL)

Arguments

Details

Let $(s_1, \ldots, s_d)$ be the sampling order of a random vector $V = (V_1, \ldots, V_d)$ with distribution $F$. For continuous variables, the Rosenblatt transform $Z = T(V)$ is defined by

$Z_{s_1} = F_{s_1}(V_{s_1}), \qquad Z_{s_j} = F_{s_j \mid s_1, \ldots, s_{j-1}} (V_{s_j} \mid V_{s_1}, \ldots, V_{s_{j-1}}), \quad j = 2, \ldots, d.$

If the model is correct, the components of $Z$ are independent standard uniforms. The result is stored in the original variable columns; the sampling order determines only the sequence of conditional distributions.

The inverse transform applies the corresponding conditional quantiles:

$V_{s_1} = F_{s_1}^{-1}(Z_{s_1}), \qquad V_{s_j} = F_{s_j \mid s_1, \ldots, s_{j-1}}^{-1} (Z_{s_j} \mid V_{s_1}, \ldots, V_{s_{j-1}}), \quad j = 2, \ldots, d.$

Thus $T^{-1}(Z)$ has distribution $F$ when $Z$ contains independent standard uniforms.

If a variable has atoms, its conditional cdf jumps at the observation. Let $G_j$ denote the conditional cdf of $V_{s_j}$ given the preceding variables in the sampling order. Following Brockwell (10.1016/j.spl.2007.02.008), rosenblatt() returns

$Z_{s_j} = W_j G_j(V_{s_j}) + (1 - W_j) G_j(V_{s_j}^{-}),$

where $G_j(V_{s_j}^{-})$ is the left limit and the $W_j$ are independent standard uniforms. This randomization is used by default and yields uniform components under the fitted model. Set randomize_discrete = FALSE to return the upper endpoint $G_j(V_{s_j})$ instead.

Examples

# simulate data with some dependence
x <- replicate(3, rnorm(200))
x[, 2:3] <- x[, 2:3] + x[, 1]
pairs(x)

# estimate a vine distribution model
fit <- vine(x, copula_controls = list(family_set = "par"))

# transform into independent uniforms
u <- rosenblatt(x, fit)
pairs(u)

# inversion
pairs(inverse_rosenblatt(u, fit))

# works similarly for vinecop models
vc <- fit$copula
rosenblatt(pseudo_obs(x), vc)

Description

R-vine structures are compressed representations encoding the tree structure of the vine, i.e. the conditioned/conditioning variables of each edge. The functions ⁠[cvine_structure()]⁠ or ⁠[dvine_structure()]⁠ give a simpler way to construct C-vines (every tree is a star) and D-vines (every tree is a path), respectively (see Examples).

Usage

rvine_structure(order, struct_array = list(), is_natural_order = FALSE)

cvine_structure(order, trunc_lvl = Inf)

dvine_structure(order, trunc_lvl = Inf)

rvine_matrix(matrix)

Arguments

Details

The R-vine structure is essentially a lower-triangular matrix/triangular array, with a notation that differs from the one in the VineCopula package. An example array is

4 4 4 4
3 3 3
2 2
1

which encodes the following pair-copulas:

An R-vine structure can be converted to an R-vine matrix using as_rvine_matrix(), which encodes the same model with a square matrix filled with zeros. For instance, the matrix corresponding to the structure above is:

4 4 4 4
3 3 3 0
2 2 0 0
1 0 0 0

Similarly, an R-vine matrix can be converted to an R-vine structure using as_rvine_structure().

Denoting by M[i, j] the array entry in row i and column j (the pair-copula index for edge e in tree t of a d dimensional vine is ⁠(M[d + 1 - e, e], M[t, e]; M[t - 1, e], ..., M[1, e])⁠. Less formally,

1. Start with the counter-diagonal element of column e (first conditioned variable).

2. Jump up to the element in row t (second conditioned variable).

3. Gather all entries further up in column e (conditioning set).

Internally, the diagonal is stored separately from the off-diagonal elements, which are stored as a triangular array. For instance, the off-diagonal elements off the structure above are stored as

4 4 4
3 3
2

for the structure above. The reason is that it allows for parsimonious representations of truncated models. For instance, the 2-truncated model is represented by the same diagonal and the following truncated triangular array:

4 4 4
3 3

A valid R-vine structure or matrix must satisfy several conditions which are checked when rvine_structure(), rvine_matrix(), or some coercion methods (see as_rvine_structure() and ⁠as_rvine_matrix(⁠) are called:

1. It can only contain numbers between 1 and d (and additionally zeros for R-vine matrices).

2. The anti-diagonal must contain the numbers 1, ..., d.

3. The anti-diagonal entry of a column must not be contained in any column further to the right.

4. The entries of a column must be contained in all columns to the left.

5. The proximity condition must hold: For all t = 1, ..., d - 2 and e = 1, ..., d - t there must exist an index j > d, such that ⁠(M[t, e], {M[1, e], ..., M[t - 1, e]})⁠ equals either ⁠(M[d + 1 - j, j], {M[1, j], ..., M[t - 1, j]})⁠ or ⁠(M[t - 1, j], {M[d + 1 - j, j], M[1, j], ..., M[t - 2, j]})⁠.

Condition 5 already implies conditions 2-4, but is more difficult to check by hand.

Value

Either an rvine_structure or an rvine_matrix.

See Also

as_rvine_structure(), as_rvine_matrix(), plot.rvine_structure(), plot.rvine_matrix(), rvine_structure_sim(), rvine_matrix_sim()

Examples

# R-vine structures can be constructed from the order vector and struct_array
rvine_structure(order = 1:4, struct_array = list(
  c(4, 4, 4),
  c(3, 3),
  2
))

# R-vine matrices can be constructed from standard matrices
mat <- matrix(c(4, 3, 2, 1, 4, 3, 2, 0, 4, 3, 0, 0, 4, 0, 0, 0), 4, 4)
rvine_matrix(mat)

# coerce to R-vine structure
str(as_rvine_structure(mat))

# truncate and construct the R-vine matrix
mat[3, 1] <- 0
rvine_matrix(mat)

# or use directly the R-vine structure constructor
rvine_structure(order = 1:4, struct_array = list(
  c(4, 4, 4),
  c(3, 3)
))

# throws an error
mat[3, 1] <- 5
try(rvine_matrix(mat))

# C-vine structure
cvine <- cvine_structure(1:5)
cvine
plot(cvine)

# D-vine structure
dvine <- dvine_structure(c(1, 4, 2, 3, 5))
dvine
plot(dvine)

Description

Simulates from a uniform distribution over all R-vine structures on d variables. rvine_structure_sim() returns an rvine_structure() object, rvine_matrix_sim() an rvine_matrix().

Usage

rvine_structure_sim(d, natural_order = FALSE)

rvine_matrix_sim(d, natural_order = FALSE)

Arguments

See Also

rvine_structure(), rvine_matrix(), plot.rvine_structure(), plot.rvine_matrix()

Examples

rvine_structure_sim(10)

rvine_structure_sim(10, natural_order = TRUE)  # counter-diagonal is 1:d

rvine_matrix_sim(10)

Description

rvinecopulib provides high-performance tools for constructing, fitting, selecting, simulating, and visualizing bivariate and vine copula models. It also fits complete multivariate distributions by combining marginal models with a vine copula. Continuous, integer-valued discrete, mixed, and zero-inflated variables are supported.

Details

The package is the R interface to the header-only 'vinecopulib' C++ library, which is bundled with rvinecopulib. Users do not need to install the C++ library separately. 'vinecopulib' is licensed under the MIT License and rvinecopulib under the GNU GPL version 3.

Main capabilities

rvinecopulib provides:

• parametric, nonparametric, rotated, and extreme-value pair copulas;

• automatic pair-copula family, vine structure, truncation, and threshold selection;

• complete multivariate distributions with nonparametric, parametric, or custom margins;

• continuous, integer-valued discrete, mixed, and zero-inflated data;

• observation weights and parallel fitting;

• conditional simulation and Rosenblatt transforms;

• likelihood scores, Hessians, and observation-specific parameters;

• custom marginal families and custom tree-selection criteria.

Modeling interfaces

The framework is exposed at three levels. vine() models observations on their original scale by fitting the marginal distributions and dependence model together. vinecop() models uniform pseudo-observations when only the dependence model is required. bicop() provides the corresponding two-variable workflow.

The constructors vine_dist(), vinecop_dist(), and bicop_dist() create models from explicitly specified components.

Learning more

Start with vignette("getting-started"). Dedicated articles cover bivariate copulas, vine structures and selection, marginal models, discrete data, conditional simulation, and likelihood derivatives. The package website contains the rendered articles and complete function reference.

Author(s)

Thomas Nagler, Thibault Vatter

See Also

Useful links:

• https://vinecopulib.github.io/rvinecopulib/

• Report bugs at https://github.com/vinecopulib/rvinecopulib/issues

Examples

## complete distribution on the original data scale
x <- data.frame(a = rnorm(50), b = rexp(50), n = rpois(50, 2))
fit <- vine(
  x,
  var_types = c("c", "c", "d"),
  copula_controls = list(family_set = "onepar")
)
rvine(3, fit)

## dependence model on the copula scale
u <- pseudo_obs(as.matrix(USArrests))
copula_fit <- vinecop(u, family_set = "onepar")
summary(copula_fit)

## bivariate model
uv <- rbicop(100, "gaussian", 0, 0.5)
bicop(uv, family_set = "par")

Description

stats_margin() adapts the univariate distributions documented in stats::Distributions to the fitted-margin protocol. Distribution parameters are supplied through ...; the resulting margin reports the distribution's parameter count but has no fitted log-likelihood.

Usage

stats_margin(family, ...)

Arguments

Value

A fixed margin implementing the margin protocol.

Examples

normal <- stats_margin("norm", mean = 1, sd = 2)
poisson <- stats_margin("pois", lambda = 3)
margin_info(normal)
margin_info(poisson)

Description

Extracts a truncated sub-vine based on a truncation level supplied by user.

Usage

truncate_model(object, ...)

## S3 method for class 'rvine_structure'
truncate_model(object, trunc_lvl, ...)

## S3 method for class 'rvine_matrix'
truncate_model(object, trunc_lvl, ...)

## S3 method for class 'vinecop_dist'
truncate_model(object, trunc_lvl, ...)

## S3 method for class 'vine_dist'
truncate_model(object, trunc_lvl, ...)

Arguments

Details

While a vine model for a d dimensional random vector contains at most d-1 nested trees, this function extracts a sub-model based on a given truncation level.

For instance, truncate_model(object, 1) results in a 1-truncated vine (i.e., a vine with a single tree). Similarly truncate_model(object, 2) results in a 2-truncated vine (i.e., a vine with two trees). Note that truncate_model(truncate_model(object, 1), 2) returns a 1-truncated vine.

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 structure
structure <- as_rvine_structure(mat)

# truncate the model
truncate_model(structure, 1)

# set up vine copula model
vc <- vinecop_dist(pcs, mat)

# truncate the model
truncate_model(vc, 1)

Description

univariateML_family() adapts one of univariateML's available models to the margin-family protocol.

Usage

univariateML_family(family)

Arguments

Value

A parametric margin-family specification.

Description

Automated fitting or creation of custom vine copula models

Usage

vine(
  data,
  margins_controls = list(),
  copula_controls = list(family_set = "all", structure = NA, par_method = "mle",
    nonpar_method = "constant", mult = 1, selcrit = "aic", psi0 = 0.9, presel = TRUE,
    allow_rotations = TRUE, trunc_lvl = Inf, tree_crit = "tau", threshold = 0, keep_data
    = FALSE, show_trace = FALSE, cores = 1, tree_algorithm = "mst_prim", conditioning_set
    = NULL),
  weights = numeric(),
  keep_data = FALSE,
  cores = 1,
  var_types = NULL
)

vine_dist(margins, pair_copulas, structure)

Arguments

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. Margin-specific fitting options are properties of the family specification, not controls interpreted by vine(). For example, use kde1d_family(xmin = 0, mult = 2) in family_set. copula_controls is a list with the same parameters as vinecop() (except for data).

A character margins_controls$family_set is used for every variable. Its entries can be "kde1d" or names of models available in univariateML. A list supplies one candidate set per variable, for example list(c("norm", "cauchy"), c("pois", "geom")). Each entry may itself be a character vector, a margin-family object, or a list combining both. An unnamed list is matched by position; a named list must contain every expanded variable name exactly once and is reordered to match the data.

The aliases "par" and "parametric" expand to all univariateML families, "nonpar" and "nonparametric" select "kde1d", and "all" combines both. Parametric names and aliases require the suggested univariateML package; the default "kde1d", kde1d_family(), and custom margin_family() objects do not. Identical candidate specifications are fitted only once.

Candidates incompatible with a variable's type are removed before fitting. rvinecopulib fits every remaining candidate and selects the first minimum of negative log-likelihood, AIC, or BIC according to selcrit. Competing candidates therefore need a finite margin_info()$loglik value, and AIC or BIC additionally requires a finite margin_info()$npars. A sole candidate without a parameter count is retained with a warning, but model AIC and BIC are then unavailable. An unsupported candidate may fail while another candidate succeeds; such failures and all candidate warnings are reported. Protocol violations always stop fitting. The built-in univariateML family explicitly rejects observation weights.

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 class vinecop_dist, see vinecop_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, a list with the controls used to fit and select the marginal families.

• copula_controls, a list with the set of fit controls that was passed to vinecop() when estimating the copula.

• data (optionally, if keep_data = TRUE was used), the dataset that was passed to vine().

• nobs, an integer containing the number of observations that was used to fit the model.

Concerning margins:

• For objects created with vine_dist(), it simply corresponds to the margins argument.

• For objects created with vine(), it is a list of fitted objects implementing the margin protocol.

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(stats_margin("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)

# parametric margin selection (requires the suggested univariateML package)
if (requireNamespace("univariateML", quietly = TRUE)) {
  fit_par_margins <- vine(
    x,
    margins_controls = list(family_set = c("norm", "cauchy"))
  )
}

## 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)

Description

Density, distribution function and random generation for the vine based distribution.

Usage

dvine(x, vine, cores = 1, log = FALSE)

pvine(x, vine, n_mc = 10^4, cores = 1)

rvine(n, vine, qrng = FALSE, cores = 1, x_cond = NULL, conditioning_set = NULL)

Arguments

Details

See vine for the estimation and construction of vine models. Here, the density, distribution function and random generation for the vine distributions are standard.

The functions are based on dvinecop(), pvinecop() and rvinecop() for vinecop objects. Margins are evaluated through dmargin(), pmargin(), and qmargin(). Methods are provided for margins fitted by vine() and for the fixed stats::Distributions specifications accepted by vine_dist().

Value

dvine() gives the density, pvine() gives the distribution function, and rvine() generates unconditional or conditional random deviates.

The length of the result is determined by n for rvine(), and the number of rows in u for the other functions.

The vine object is recycled to the length of the result.

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
)

# set up vine copula model
mat <- rvine_matrix_sim(3)
vc <- vine_dist(list(stats_margin("norm")), pcs, mat)

# simulate from the model
x <- rvine(200, vc)
pairs(x)

# evaluate the density and cdf
dvine(x[1, ], vc)
pvine(x[1, ], vc)

Description

Predictions of the density and distribution function for a vine copula model.

Usage

## S3 method for class 'vine'
predict(object, newdata, what = "pdf", n_mc = 10^4, cores = 1, ...)

## S3 method for class 'vine'
fitted(object, what = "pdf", n_mc = 10^4, cores = 1, ...)

Arguments

Value

fitted() and predict() have return values similar to dvine() and pvine().

Examples

x <- sapply(1:5, function(i) rnorm(50))
fit <- vine(x, copula_controls = list(family_set = "par"), keep_data = TRUE)
all.equal(predict(fit, x), fitted(fit), check.environment = FALSE)

Description

Automated fitting and model selection for vine copula models with continuous or discrete data. Selection of the structure is performed using the algorithm of Dissmann et al. (2013).

Usage

vinecop(
  data,
  var_types = rep("c", NCOL(data)),
  family_set = "all",
  structure = NA,
  par_method = "mle",
  nonpar_method = "constant",
  mult = 1,
  selcrit = "aic",
  weights = numeric(),
  psi0 = 0.9,
  presel = TRUE,
  allow_rotations = TRUE,
  trunc_lvl = Inf,
  tree_crit = "tau",
  threshold = 0,
  keep_data = FALSE,
  vinecop_object = NULL,
  show_trace = FALSE,
  cores = 1,
  tree_algorithm = "mst_prim",
  conditioning_set = NULL
)

Arguments

Details

Missing data

If there are missing data (i.e., NA entries), incomplete observations are discarded before fitting a pair-copula. This is done on a pair-by-pair basis so that the maximal available information is used.

Discrete variables

The dependence measures used to select trees (default: Kendall's tau) are corrected for ties (see wdm::wdm).

Let n be the number of observations and d the number of variables. When at least one variable is discrete, two types of "observations" are required in data: the first ⁠n x d⁠ block contains realizations of $F_{X_j}(X_j)$. The second ⁠n x d⁠ block contains realizations of $F_{X_j}(X_j^-)$. The minus indicates a left-sided limit of the cdf. For, e.g., an integer-valued variable, it holds $F_{X_j}(X_j^-) = F_{X_j}(X_j - 1)$. For continuous variables the left limit and the cdf itself coincide. Respective columns can be omitted in the second block.

Structure selection

Selection of the structure is performed using the algorithm of Dissmann, J. F., E. C. Brechmann, C. Czado, and D. Kurowicka (2013). Selecting and estimating regular vine copulae and application to financial returns. Computational Statistics & Data Analysis, 59 (1), 52-69. The dependence measure used to select trees (default: Kendall's tau) is corrected for ties and can be changed using the tree_crit argument, which can be set to "tau", "rho", "hoeffd", "mcor", "cxi", or "joe", or to a custom function. Both Prim's (default: "mst_prim") and Kruskal's ("mst_kruskal") algorithms are available through tree_algorithm to set the maximum spanning tree selection algorithm. An alternative to the maximum spanning tree selection is to use random spanning trees, which can be selected using tree_algorithm and come in two flavors, both using Wilson's algorithm loop erased random walks:

• "random_weighted"' generates a random spanning tree with probability proportional to the product of the weights (i.e., the dependence) of the edges in the tree.

• "random_unweighted"' generates a random spanning tree uniformly over all spanning trees satisfying the proximity condition.

Partial structure selection

It is possible to fix the vine structure only in the first trees and select the remaining ones automatically. To specify only the first k trees, supply a k-truncated rvine_structure() or rvine_matrix(). All trees up to trunc_lvl will then be selected automatically.

Value

Objects inheriting from vinecop and vinecop_dist for vinecop(). In addition to the entries provided by vinecop_dist(), there are:

• threshold, the (set or estimated) threshold used for thresholding the vine.

• data (optionally, if keep_data = TRUE was used), the dataset that was passed to vinecop().

• controls, a list with fit controls that was passed to vinecop().

• nobs, the number of observations that were used to fit the model.

References

Dissmann, J. F., E. C. Brechmann, C. Czado, and D. Kurowicka (2013). Selecting and estimating regular vine copulae and application to financial returns. Computational Statistics & Data Analysis, 59 (1), 52-69.

See Also

vinecop(), dvinecop(), pvinecop(), rvinecop(), plot.vinecop(), contour.vinecop()

Examples

## simulate dummy data
x <- rnorm(30) * matrix(1, 30, 5) + 0.5 * matrix(rnorm(30 * 5), 30, 5)
u <- pseudo_obs(x)

## fit and select the model structure, family and parameters
fit <- vinecop(u)
summary(fit)
plot(fit)
contour(fit)

## select by BIC from one-parameter families
fit <- vinecop(u, family_set = "onepar", selcrit = "bic")
summary(fit)

## 1-truncated, Gaussian D-vine
fit <- vinecop(
  u,
  structure = dvine_structure(1:5),
  family_set = "gauss",
  trunc_lvl = 1
)
plot(fit)
contour(fit)

## Partial structure selection with only first tree specified
structure <- rvine_structure(order = 1:5, list(rep(5, 4)))
structure
fit <- vinecop(u, structure = structure, family_set = "gauss")
plot(fit)

## Model for discrete data
x <- qpois(u, 1)  # transform to Poisson margins
# we require two types of observations (see Details)
u_disc <- cbind(ppois(x, 1), ppois(x - 1, 1))
fit <- vinecop(u_disc, var_types = rep("d", 5))

## Model for mixed data
x <- qpois(u[, 1], 1)  # transform first variable to Poisson margin
# we require two types of observations (see Details)
u_disc <- cbind(ppois(x, 1), u[, 2:5], ppois(x - 1, 1))
fit <- vinecop(u_disc, var_types = c("d", rep("c", 4)))

Description

Create custom vine copula models by specifying the pair-copulas, structure, and variable types.

Usage

vinecop_dist(pair_copulas, structure, var_types = rep("c", dim(structure)[1]))

Arguments

Value

Object of class vinecop_dist, i.e., a list containing:

• pair_copulas, a list of lists. Each element of pair_copulas corresponds to a tree, which is itself a list of bicop_dist() objects.

• structure, a compressed representation of the vine structure, or an object that can be coerced into one (see rvine_structure() and as_rvine_structure()).

• npars, a numeric with the number of (effective) parameters.

• var_types the variable types.

See Also

rvine_structure(), rvine_matrix(), vinecop(), plot.vinecop_dist(), contour.vinecop_dist(), dvinecop(), pvinecop(), rvinecop()

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
vc <- vinecop_dist(pcs, mat)

# visualization
plot(vc)
contour(vc)

# simulate from the model
pairs(rvinecop(200, vc))

Description

Density, distribution function and random generation for the vine copula distribution.

Usage

dvinecop(
  u,
  vinecop,
  cores = 1,
  keep_all = FALSE,
  parameters = NULL,
  log = FALSE
)

scores(u, vinecop, ...)

## S3 method for class 'vinecop_dist'
scores(u, vinecop, step_wise = TRUE, cores = 1, parameters = NULL, ...)

hessian(u, vinecop, ...)

## S3 method for class 'vinecop_dist'
hessian(u, vinecop, step_wise = TRUE, cores = 1, parameters = NULL, ...)

pvinecop(u, vinecop, n_mc = 10^4, cores = 1)

rvinecop(
  n,
  vinecop,
  qrng = FALSE,
  cores = 1,
  u_cond = NULL,
  conditioning_set = NULL
)

Arguments

Details

See vinecop() for the estimation and construction of vine copula models.

The copula density is defined as joint density divided by marginal densities, irrespective of variable types.

Discrete variables

When at least one variable is discrete, two types of "observations" are required in u: the first $n \; x \; d$ block contains realizations of $F_{X_j}(X_j)$. The second $n \; x \; d$ block contains realizations of $F_{X_j}(X_j^-)$. The minus indicates a left-sided limit of the cdf. For, e.g., an integer-valued variable, it holds $F_{X_j}(X_j^-) = F_{X_j}(X_j - 1)$. For continuous variables the left limit and the cdf itself coincide. Respective columns can be omitted in the second block.

Value

dvinecop() gives the density, pvinecop() gives the distribution function, rvinecop() generates unconditional or conditional random deviates, scores() gives the observation-wise score matrix, and hessian() gives the average Hessian matrix.

If keep_all = TRUE, dvinecop() returns a list with entries pdf, logpdf, pdf_edges, hfunc1, hfunc2, hfunc1_sub, and hfunc2_sub. The ⁠_sub⁠ entries contain h-functions evaluated at left-sided limits when the model contains discrete variables; they are empty for fully continuous models.

The length of the result is determined by n for rvinecop(), and the number of rows in u for the other functions.

The vinecop object is recycled to the length of the result.

See Also

vinecop_dist(), vinecop(), plot.vinecop(), contour.vinecop()

Examples

## simulate dummy data
x <- rnorm(30) * matrix(1, 30, 5) + 0.5 * matrix(rnorm(30 * 5), 30, 5)
u <- pseudo_obs(x)

## fit a model
vc <- vinecop(u, family_set = "clayton")

# simulate from the model
u <- rvinecop(100, vc)
pairs(u)

# evaluate the density and cdf
dvinecop(u[1, ], vc)
pvinecop(u[1, ], vc)

# evaluate derivatives of the log-likelihood
scores(u, vc)
hessian(u, vc)

# derivatives can also be computed for the full likelihood
scores(u, vc, step_wise = FALSE)
hessian(u, vc, step_wise = FALSE)

## Discrete models
vc$var_types <- rep("d", 5)  # convert model to discrete

# with discrete data we need two types of observations (see Details)
x <- qpois(u, 1)  # transform to Poisson margins
u_disc <- cbind(ppois(x, 1), ppois(x - 1, 1))

dvinecop(u_disc[1:5, ], vc)
pvinecop(u_disc[1:5, ], vc)

# simulated data always has uniform margins
pairs(rvinecop(200, vc))

## Conditional simulation
vc_cond <- vinecop(
  u,
  family_set = "gaussian",
  conditioning_set = c(2, 4)
)
u_cond <- c(0.25, 0.75)
uc <- rvinecop(
  100,
  vc_cond,
  u_cond = u_cond,
  conditioning_set = c(2, 4)
)
stopifnot(
  isTRUE(all.equal(uc[, 2], rep(0.25, 100))),
  isTRUE(all.equal(uc[, 4], rep(0.75, 100)))
)

Description

Predictions of the density and distribution function for a vine copula model.

Usage

## S3 method for class 'vinecop'
predict(object, newdata, what = "pdf", n_mc = 10^4, cores = 1, ...)

## S3 method for class 'vinecop'
fitted(object, what = "pdf", n_mc = 10^4, cores = 1, ...)

Arguments

Details

fitted() can only be called if the model was fit with the keep_data = TRUE option.

Discrete variables

When at least one variable is discrete, two types of "observations" are required in newdata: the first $n \; x \; d$ block contains realizations of $F_{X_j}(X_j)$. The second $n \; x \; d$ block contains realizations of $F_{X_j}(X_j^-)$. The minus indicates a left-sided limit of the cdf. For, e.g., an integer-valued variable, it holds $F_{X_j}(X_j^-) = F_{X_j}(X_j - 1)$. For continuous variables the left limit and the cdf itself coincide. Respective columns can be omitted in the second block.

Value

fitted() and predict() have return values similar to dvinecop() and pvinecop().

Examples

u <- sapply(1:5, function(i) runif(50))
fit <- vinecop(u, family_set = "par", keep_data = TRUE)
all.equal(predict(fit, u), fitted(fit), check.environment = FALSE)