R: RRdistr

RRdistr {RandomFields} | R Documentation

## RRdistr

### Description

`RRdistr` defines of distribution family given by `fct`. It is used to introduce Random parameters based on distributions defined on R.

### Usage

    RRdistr(fct, nrow, ncol, envir)

### Arguments

`fct` | an arbitrary family of distribution. E.g. `norm()` for the family `dnorm`, `pnorm`, `qnorm`, `rnorm`.
`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()`.

### 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 some 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 of obsolete commands of RandomFields.

### Author(s)

Martin Schlather, schlather@math.uni-mannheim.de http://ms.math.uni-mannheim.de/de/publications/software

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

[Package RandomFields version 3.1.50 Index]