R: Bayesian Spatial Modelling

Hierarchical Modelling {RandomFields} | R Documentation

## Bayesian Spatial Modelling

### Description

RandomFields provides Bayesian modelling to some extend: (i) simulation of hierarchical models at arbitrary depth; (ii) estimation of the parameteres of a hierarchical model of depth 1 by means of maximizing the likelihood.

### Details

A Bayesian approach can be taken for scalar, real valued model parameters, e.g. the shape parameter `nu` in the RMmatern model. A random parameter can be passed through a distribution of an existing family, e.g. (`dnorm`, `pnorm`, `qnorm`, `rnorm`) or self-defined. It is passed without the leading letter `d`, `p`, `q`, `r`, but as a function call e.g `norm()`. This function call may contain arguments that must be named, e.g. `norm(mean=3, sd=5)`.

Usage:

- `exp()` denotes the exponential distribution family with rate 1,

- `exp(3)` is just the scalar e^3 and

- `exp(rate=3)` is the exponential distribution family with rate 3.

The family can be passed in three ways:

- implicitelty, e.g. `RMwhittle(nu=exp())` or

- explicitely through `RRdistr`, e.g. `RMwhittle(nu=RRdistr(exp()))`.

- by use of `RRmodels` of the package

The first is more convenient, the second more flexible and slightly safer.

### Note

- While simulating any depth of hierarchical modelling is possible, estimation is currently restricted to one level of hierarchy.

- The effect of the distribution family varies between the different processes:

  - in Max-stable fields and `RPpoisson`, a new realisation of the prior distribution(s) is drawn for each shape function

  - in all the other cases: a realisation of the prior(s) is only drawn once. This effects, in particular, Gaussian fields with argument `n>1`, where all the realisations are based on the same realisation out of the prior distribution(s).

  Note that checking the validity of the arguments is rather limited for such complicated models, in general.

### Author(s)

Martin Schlather, schlather@math.uni-mannheim.de

### See Also

RMmodelsAdvanced For hierarchical modelling see RR

### Examples

    RFoptions(seed=0) ## *ANY* simulation will have the random seed 0; set
    ##                   RFoptions(seed=NA) to make them all random again

    ## See 'RRmodels'for hierarchical models

    ## the following model defines the argument nu of the Whittle-Matern
    ## model to be an expontential random variable with rate 5.
    model <- ~ 1 + RMwhittle(scale=NA, var=NA, nu=exp(rate=5)) + RMnugget(var=NA)

    data(soil)
    fit <- RFfit(model, x=soil$x, y=soil$y, data=soil$moisture, modus="careless")
    print(fit)

[Package RandomFields version 3.1.50 Index]