Package 'rvinecopulib'

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

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,
  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 $F_{X_1}(x_1^-), F_{X_1}(x_1^-)$. 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"),

• "elliptical" contains the elliptical families,

• "archimedean" contains the archimedean 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

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"))

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

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

hbicop(
  u,
  cond_var,
  family,
  rotation,
  parameters,
  inverse = FALSE,
  var_types = c("c", "c")
)

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.

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 $F_{X_1}(x_1^-), F_{X_1}(x_1^-)$. 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, pbicop() gives the distribution function, rbicop() generates random deviates, and hbicop() gives the h-functions (and their inverses).

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.

Note

The functions can optionally be used with a bicop_dist object, e.g., dbicop(c(0.1, 0.5), 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)

## 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 $F_{X_1}(x_1^-), F_{X_1}(x_1^-)$. 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 = "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

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

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

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.pdf)

Examples

u <- sapply(1:5, function(i) runif(50))
fit <- vinecop(u, family = "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, ...)

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

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(), 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 = "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 takes data generated from a model and turns it into independent uniform variates, The inverse Rosenblatt transform computes conditional quantiles and can be used simulate from a stochastic model, see Details.

Usage

rosenblatt(x, model, cores = 1)

inverse_rosenblatt(u, model, cores = 1)

Arguments

Details

The Rosenblatt transform (Rosenblatt, 1952) $U = T(V)$ of a random vector $V = (V_1,\ldots,V_d) ~ F$ is defined as

$U_1= F(V_1), U_{2} = F(V_{2}|V_1), \ldots, U_d =F(V_d|V_1,\ldots,V_{d-1}),$

where $F(v_k|v_1,\ldots,v_{k-1})$ is the conditional distribution of $V_k$ given $V_1 \ldots, V_{k-1}, k = 2,\ldots,d$. The vector $U = (U_1, \dots, U_d)$ then contains independent standard uniform variables. The inverse operation

$V_1 = F^{-1}(U_1), V_{2} = F^{-1}(U_2|U_1), \ldots, V_d =F^{-1}(U_d|U_1,\ldots,U_{d-1}),$

can be used to simulate from a distribution. For any copula $F$, if $U$ is a vector of independent random variables, $V = T^{-1}(U)$ has distribution $F$.

The formulas above assume a vine copula model with order $d, \dots, 1$. More generally, rosenblatt() returns the variables

$U_{M[d + 1- j, j]}= F(V_{M[d + 1- j, j]} | V_{M[d - j, j - 1]}, \dots, V_{M[1, 1]}),$

where $M$ is the structure matrix. Similarly, inverse_rosenblatt() returns

$V_{M[d + 1- j, j]}= F^{-1}(U_{M[d + 1- j, j]} | U_{M[d - j, j - 1]}, \dots, U_{M[1, 1]}).$

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

Provides an interface to 'vinecopulib', a C++ library for vine copula modeling based on 'Boost' and 'Eigen'. The 'rvinecopulib' package implements the core features of the popular 'VineCopula' package, in particular inference algorithms for both vine copula and bivariate copula models. Advantages over 'VineCopula' are a sleeker and more modern API, improved performances, especially in high dimensions, nonparametric and multi-parameter families. The 'rvinecopulib' package includes 'vinecopulib' as header-only C++ library (currently version 0.6.2). Thus users do not need to install 'vinecopulib' itself in order to use 'rvinecopulib'. Since their initial releases, 'vinecopulib' is licensed under the MIT License, and 'rvinecopulib' is licensed under the GNU GPL version 3.

Author(s)

Thomas Nagler, Thibault Vatter

Examples

## bicop_dist objects
bicop_dist("gaussian", 0, 0.5)
str(bicop_dist("gauss", 0, 0.5))
bicop <- bicop_dist("clayton", 90, 3)

## bicop objects
u <- rbicop(500, "gauss", 0, 0.5)
fit1 <- bicop(u, family = "par")
fit1

## vinecop_dist objects
## 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)
## build the vinecop_dist object
vc <- vinecop_dist(pcs, mat)
summary(vc)

## vinecop objects
u <- sapply(1:3, function(i) runif(50))
vc <- vinecop(u, family = "par")
summary(vc)

## vine_dist objects
vc <- vine_dist(list(distr = "norm"), pcs, mat)
summary(vc)

## vine objects
x <- sapply(1:3, function(i) rnorm(50))
vc <- vine(x, copula_controls = list(family_set = "par"))
summary(vc)

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

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

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 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 set of fit controls that was passed to kde1d::kde1d() when estimating the margins.

• 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 objects of class kde1d, see kde1d::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)

Description

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

Usage

dvine(x, vine, cores = 1)

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

rvine(n, vine, qrng = FALSE, cores = 1)

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, and either kde1d::dkde1d(), kde1d::pkde1d() and kde1d::qkde1d() for estimated vines (i.e., output of vine()), or the standard d/p/q-xxx from stats::Distributions for custom vines (i.e., output of vine_dist()).

Value

dvine() gives the density, pvine() gives the distribution function, and rvine() generates 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(distr = "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,
  trunc_lvl = Inf,
  tree_crit = "tau",
  threshold = 0,
  keep_data = FALSE,
  show_trace = FALSE,
  cores = 1
)

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.

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 log-likelihood criterion from one-paramter families
fit <- vinecop(u, family_set = "onepar", selcrit = "bic")
summary(fit)

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

## 1-truncated model with random structure
fit <- vinecop(u, structure = rvine_structure_sim(5), trunc_lvl = 1)
contour(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)

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

rvinecop(n, vinecop, qrng = FALSE, cores = 1)

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, and rvinecop() generates random deviates.

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 = "clayton")

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

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

## 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))

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 = "par", keep_data = TRUE)
all.equal(predict(fit, u), fitted(fit), check.environment = FALSE)