rvinecopulib (version 0.7.2.1.0)

vinecop_predict_and_fitted: Predictions and fitted values for a vine copula model

Description

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

Usage

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

Value

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

and pvinecop().

Arguments

object

a vinecop object.

newdata

points where the fit shall be evaluated.

what

what to predict, either "pdf" or "cdf".

n_mc

number of samples used for quasi Monte Carlo integration when what = "cdf".

cores

number of cores to use; if larger than one, computations are done in parallel on cores batches.

...

unused.

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.

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)