# predict_vinecop: Predictions and fitted values for a vine copula model In rvinecopulib: High Performance Algorithms for Vine Copula Modeling

 vinecop_predict_and_fitted R Documentation

## 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.