unuran.new {Runuran}

Create a UNU.RAN object

Description

Create a new unuran object in package Runuran that can be used for sampling from the specified distribution. The function ur can then be used to draw a random sample.

[Advanced] – Create generator object.

Usage

unuran.new(distr,method="auto")

Arguments

distr

a string or an S4 class describing the distribution.

method

a string describing the random variate generation method.

Details

This function creates an instance of S4 class unuran which contains a generator for the target distribution. This distribution has to be provided as an instance of S4 class unuran.distr. Depending on the type of distribution such an instance can be created by

unuran.cont.new

for univariate continuous distributions,

unuran.discr.new

for discrete distributions, and

unuran.cmv.new

for multivariate continuous distributions.

The generation can be chosen by passing method to the UNU.RAN String API. The default method, "auto" tries to find an appropriate method for the given distribution. However, this method is experimental and is yet not very powerfull.

Once a unuran object has been created it can be used to draw random samples from the target distribution using ur.

Author(s)

Josef Leydold and Wolfgang H\"ormann [e-mail].

References

W. H\"ormann, J. Leydold, and G. Derflinger (2004): Automatic Nonuniform Random Variate Generation. Springer-Verlag, Berlin Heidelberg.

See Also

See unuran for the UNU.RAN class of generators. See unuran.details for printing details about the generator object, and ur and uq for sampling and quantile function, respectively.

For distribution objects see unuran.cont, unuran.discr, and unuran.cmv.

runif, .Random.seed about random number generation in R.

Examples

## Use method 'TDR' (Transformed Density Rejection) to 
## draw a sample of size 10 from a hyperbolic distribution with PDF
##   f(x) = const * exp(-sqrt(1+x^2)) 
## restricted to domain [-1,2].

## We first have to define functions that return the log-density and
## its derivative, respectively. (We also could use the density itself.)
lf  <- function (x) { -sqrt(1+x^2) }
dlf <- function (x) { -x/sqrt(1+x^2) }

## Next create the continuous distribution object.
d <- unuran.cont.new(pdf=lf,dpdf=dlf,islog=TRUE,lb=-1,ub=2)

## Create 'unuran' object. We choose method 'TDR' with 
## immediate acceptance (IA) and parameter c=0.
gen <- unuran.new(distr=d, method="tdr; variant_ia; c=0")

## Now we can use this object to draw the sample.
## (Of course we can repeat this step as often as required.)
ur(gen,10)

## Here is some information about our generator object.
unuran.details(gen)