transform-extract-methods: Linear transformation and extraction of generalized...

In ghyp: Generalized Hyperbolic Distribution and Its Special Cases

Linear transformation and extraction of generalized hyperbolic distributions

Description

The transform function can be used to linearly transform generalized hyperbolic distribution objects (see Details). The extraction operator [ extracts some margins of a multivariate generalized hyperbolic distribution object.

Usage

## S4 method for signature 'ghyp'
transform(`_data`, summand, multiplier)

## S3 method for class 'ghyp'
x[i = c(1, 2)]

Arguments

_data	An object inheriting from class ghyp.
summand	A vector.
multiplier	A vector or a matrix.
x	A multivariate generalized hyperbolic distribution inheriting from class ghyp.
i	Index specifying which dimensions to extract.

Details

If X \sim GH, transform gives the distribution object of “multiplier * X + summand”, where X is the argument named _data.

If the object is of class mle.ghyp, iformation concerning the fitting procedure (cf. ghyp.fit.info) will be lost as the return value is an object of class ghyp.

Value

An object of class ghyp.

Author(s)

David Luethi

See Also

scale, ghyp, fit.ghypuv and fit.ghypmv for constructors of ghyp objects.

Examples

  ## Mutivariate generalized hyperbolic distribution
  multivariate.ghyp <- ghyp(sigma=var(matrix(rnorm(9),ncol=3)), mu=1:3, gamma=-2:0)

  ## Dimension reduces to 2
  transform(multivariate.ghyp, multiplier=matrix(1:6,nrow=2), summand=10:11)

  ## Dimension reduces to 1
  transform(multivariate.ghyp, multiplier=1:3)

  ## Simple transformation
  transform(multivariate.ghyp, summand=100:102)

  ## Extract some dimension
  multivariate.ghyp[1]
  multivariate.ghyp[c(1, 3)]

ghyp documentation built on Aug. 21, 2023, 5:12 p.m.