RRmcmc: Random Sample From The Modulus Of A Function
In RandomFields: Simulation and Analysis of Random Fields

Description Usage Arguments Value Note See Also Examples

RRmcmc draws a random sample from the modulus of any given function (provided the integral is finite).

1	RRmcmc(phi, mcmc_n, sigma, normed, maxdensity, rand.loc, gibbs)

`phi`	an arbitrary integrable function
`mcmc_n`	positive integer. Every `mcmc_n`th element of the MCMC chain is returned.
`sigma`	positive real number. The MCMC update is done by adding a normal variable with standard deviation `sigma`.
`normed`	logical. Only used if the value of the density is calculated. If `FALSE` the unnormed value given by `phi` is returned. Default: `FALSE`.
`maxdensity`	positive real number. The given density is truncated at `maxdensity`. Default: 1000.
`rand.loc`	logical. Internal. Do not change the value. Default: `FALSE`.
`gibbs`	logical. If `TRUE` only one component is updated at a time. Default: `FALSE`.

RRmcmc returns an object of class RMmodel.

The use of RRmcmc is completely on the risk of the user. There is no way to check whether the integral of the modulus is finite.

RMmodel, RR, RRdistr, RMuser.

RFoptions(seed=0) ## *ANY* simulation will have the random seed 0; set
##                   RFoptions(seed=NA) to make them all random again
## here a model with random scale parameter

## not exponential, but the Laplace distribution as symmetry is assumed
z <- RFrdistr(RRmcmc(RMexp(), sigma=1), n=10000, cores=1)
hist(z, 100, freq=FALSE)
curve(0.5 * exp(-abs(x)), add=TRUE, col="blue") ## Laplace distribution