RRdistr: Definition of Distribution Families

In RandomFields: Simulation and Analysis of Random Fields

Description

RRdistr defines a distribution family given by fct. It is used to introduce random parameters based on distributions defined on R.

Usage

RRdistr(name, nrow, ncol, 
        envir, ...)

Arguments

name	an arbitrary family of distributions. E.g. norm() for the family dnorm, pnorm, qnorm, rnorm. See examples below.
nrow, ncol	The matrix size (or vector if ncol=1) the family returns. Except for very advanced modelling we always have nrow=ncol=1, which is the default.

envir	an environment; defaults to new.env().
...	Second possibility to pass the distribution family is to pass a character string as name and to give the argument within .... See examples below.

Details

RRdistr returns an object of class RMmodel.

Note

RRdistr is the generic model introduced automatically when distribution families in R are used in the model definition. See the examples below.

Note

See Bayesian Modelling for a less technical introduction to hierarchical modelling.

The use of RRdistr is completely on the risk of the user. There is no way to check whether the expressions of the user are mathematically correct.

Further, RRdistr may not be used in connection with obsolete commands of RandomFields.

See Also

RMmodel, RR, RFsimulate, RFdistr.

Examples

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
model <- RMgauss(scale=exp(rate=1))
x <- seq(0,10,0.02)
n <- 10
 
for (i in 1:n) {  
  readline(paste("Simulation no.", i, ": press return", sep=""))
  plot(RFsimulate(model, x=x, seed=i))
}

## another possibility to define exactly the same model above is
## model <- RMgauss(scale=exp())

## note that however, the following two definitions lead
## to covariance models with fixed scale parameter:
## model <- RMgauss(scale=exp(1))   # fixed to 2.7181
## model <- RMgauss(scale=exp(x=1)) # fixed to 2.7181


## here, just two other examples:
## fst
model <- RMmatern(nu=unif(min=0.1, max=2)) # random
for (i in 1:n) {
  readline(paste("Simulation no.", i, ": press return", sep=""))
  plot(RFsimulate(model, x=x, seed=i))
}

## snd, part 1
## note that the fist 'exp' refers to the exponential function,
## the second to the exponential distribution.
(model1 <- RMgauss(var=exp(3), scale=exp(rate=1)))
x <- 1:100/10
plot(z1 <- RFsimulate(model=model, x=x))

## snd, part 2
## exactly the same result as in the previous example
(model2 <- RMgauss(var=exp(3), scale=RRdistr("exp", rate=1)))
plot(z2 <- RFsimulate(model=model, x=x))
all.equal(model1, model2)

RandomFields documentation built on Jan. 19, 2022, 1:06 a.m.