predict_vinecop: Predictions and fitted values for a vine copula model

In rvinecopulib: High Performance Algorithms for Vine Copula Modeling

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 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.

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)

rvinecopulib documentation built on March 7, 2023, 6:20 p.m.