predict_bicop: Predictions and fitted values for a bivariate copula model

In rvinecopulib: High Performance Algorithms for Vine Copula Modeling

Predictions and fitted values for a bivariate copula model

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

object	a bicop object.
newdata	points where the fit shall be evaluated.
what	what to predict, one of "pdf", "cdf", "hfunc1", "hfunc2", "hinv1", "hinv2".
...	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, 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)

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