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RandomFields (version 3.1.16)

Hierarchical Modelling: 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.

Arguments

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.

See Also

RMmodelsAdvanced For hierarchical modelling see RR

Examples

Run this code
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)




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