Predictions and fitted values for a bivariate copula model — bicop_predict_and_fitted • rvinecopulib
Description: Predictions of the density, distribution function, h-functions (with their inverses) for a bivariate copula model.

# Predictions and fitted values for a bivariate copula model

Source: `R/bicop_methods.R`
`predict_bicop.Rd`

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