Predictions and fitted values for a bivariate copula model

Predictions of the density, distribution function, h-functions (with their inverses) for a bivariate copula model.

# S3 method for bicop_dist
predict(object, newdata, what = "pdf", ...)

# S3 method for 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.

Value

fitted() and logLik() have return values similar to dbicop(), pbicop(), and hbicop().

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.

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)
#> [1] TRUE