RandomFields (version 2.0.71)

parampositions: Position of the parameters

Description

The function returns the internal positions of the model parameters

Usage

parampositions(model, param, trend=NULL, dim, print=1)

Arguments

model

see CovarianceFct

param

see CovarianceFct

trend

trend

dim

dimension of the field

print

if 0 only an invisible list is returned

Value

• The model is printed and returned where the values of the parameters are the positions in the internal representation.

  An error appears if the model can be substantially simplified.

See Also

CovarianceFct, fitvario sophisticated

Examples

#####################
## output examples ##
#####################

## basic models
parampositions(model="exp", param=c(1,2,3,NA), dim=1, print=3) 
parampositions(model="exp", param=c(0,1,NA,NA), dim=1, print=3) 

## nested 
parampositions(model="whi", param=rbind(c(1, NA, 3), c(4, 5, NA)),
               dim=1, print=3)

## complicated models
model <- list("+",
              list("$", aniso=matrix(c(5:7, NA), nc=2), var=NA, list("exp")), 
              list("$", var=2, scale=7, list("whittle", nu=NA)))
parampositions(model=model, dim=2, print=3)



########################################################
## internal estimation of log(scale) instead of scale ##
########################################################

## simulate
x <- c(0, 10, 0.1)
model <- list("$", var=1, scale=1, list("exponential"))
z <- GaussRF(x, model=model, gridtriple=TRUE, n=10)

## usual estimation
est.model <- list("+",
                 list("$", var=NA, scale=NA, list("exponential")),
                 list("$", var=NA, list("nugget")))
fit <- fitvario(x, gridtriple=TRUE, data=z, model=est.model)
str(fit$ml)

## estimating internally log(scale) instead of scale --
## the return is scale itself! However, in this setup
## the transformation leads to worse results
##
parampositions(est.model, dim=1) # indicates where scale is stored;
##                                 namely in the second position:
trafo <- function(param) {param[2] <- exp(param[2]); param}
lower <- list("+",
              list("$", var=NA, scale=-3, list("exponential")),
              list("$", var=NA, list("nugget")))
upper <- list("+",
              list("$", var=NA, scale=3, list("exponential")),
              list("$", var=NA, list("nugget")))
guess <- list("+",
              list("$", var=1, scale=0, list("exponential")),
              list("$", var=1, list("nugget")))
fitlog <- fitvario(x, gridtriple=TRUE, data=z, model=est.model,
                   transform=trafo, lower=lower, upper=upper,
                   users.guess=guess)
str(fitlog$ml) ## note that scale is returned, not log(scale) !