RMdeclare: Declaration of dummy variables for statistical inference
In RandomFields: Simulation and Analysis of Random Fields

Description Usage Arguments Value Note Author(s) See Also Examples

The only purpose of this function is the declaration of dummy variables for defining more complex relations between parameters that are to be estimated.

Its value as a covariance model is identically zero, independently of the variables declared.

1	RMdeclare(...)

...

the names of additional parameters, not in inverted commas. No values should be given.

RMdeclare returns an object of class RMmodel

Only scalars can be defined here, since only scalars can be used within formulae.

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

RMmodel, RFsimulate, RFfit.

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

## The following two examples illustrate the use of RMdeclare and the
## argument 'params'. The purpose is not to give nice statistical models 

x <- seq(1, 3, 0.1)


## note that there isn't any harm to declare variables ('u')
## RMdeclare that are of no use in a simulation
model <- ~ RMexp(sc=sc1, var=var1) + RMgauss(var=var2, sc=sc2) + RMdeclare(u)
p <- list(sc1=2, var1=3, sc2=4, var2=5)
z <- RFsimulate(model = model, x=x, y=x, params=p)
plot(z)

## note that the model remains the same, only the values in the
## following list change. Here, sc1, var1, sc2 and u are estimated
## and var2 is given by a forula.
p.fit <- list(sc1 = NA, var1=NA, var2=~2 * u, sc2 = NA, u=NA)
lower <- list(sc1=20, u=5)
upper <- list(sc2=1.5, sc1=100, u=15)
f <- RFfit(model, data=z, params=p.fit, lower = lower, upper = upper)
print(f)


## The second example shows that rather complicated constructions are
## possible, i.e., formulae involving several variables, both known ('abc')
## and unknown ones ('sc', 'var'). Note that there are two different
## 'var's a unknown variable and an argument for RMwhittle
## Not run: 
	 
model2 <- ~ RMexp(sc) + RMwhittle(var = g, nu=Nu) + 
  RMnugget(var=nugg) +  RMexp(var=var, Aniso=matrix(A, nc=2)) +
  RMdeclare(CCC, DD)
p.fit <- list(g=~sc^1.5,  nugg=~sc * var * abc, sc=NA, var=~DD, Nu=NA, abc=123,
	      A = ~c(1, 2, DD * CCC, CCC), CCC = NA, DD=NA)
lower <- list(sc=1, CCC=1, DD=1)
upper <- list(sc=100, CCC=100, DD=100)
f2 <- RFfit(model2, data=z, params=p.fit, lower = lower, upper = upper)
print(f2)

## End(Not run)