Predictions and fitted values for a vine copula model — vinecop_predict_and_fitted • rvinecopulib
Description: Predictions of the density and distribution function for a vine copula model.

# Predictions and fitted values for a vine copula model

Source: `R/vinecop_methods.R`
`predict_vinecop.Rd`

Predictions of the density and distribution function for a vine copula model.

    # S3 method for vinecop
    predict(object, newdata, what = "pdf", n_mc = 10^4, cores = 1, ...)

    # S3 method for 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