Predictions and fitted values for a vine copula model

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

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.

Value

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

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)
#> [1] TRUE