RFfit: Fitting model parameters to spatial data (regionalised...
In RandomFields: Simulation and Analysis of Random Fields

Description Usage Arguments Details Value Note References See Also Examples

The function estimates arbitrary parameters of a random field specification with various methods. Currently, the models to be fitted can be

Gaussian random fields
linear models

The fitting of max-stable random fields and others has not been implemented yet.

RFfit(model, x, y = NULL, z = NULL, T = NULL, grid=NULL, data,
      lower = NULL, upper = NULL, methods,
      sub.methods, optim.control = NULL, users.guess = NULL,
      distances = NULL, dim, transform = NULL, params=NULL, ...)

`model,params`	\argModel All parameters that are set to `NA` will be estimated; see the examples below. Type `RFgetModelNames(type="variogram")` to get all options for `model`.
`x`	\argX
`y,z`	\argYz
`T`	\argT
`grid`	\argGrid
`data`	\argData
`lower`	\argLower
`upper`	\argUpper
`methods`	\argFitmethods
`sub.methods`	\argFitsubmethods . See Details.
`users.guess`	\argUsersguess
`distances,dim`	\argDistances
`optim.control`	\argOptimcontrol
`transform`	\argTransform
`...`	\argDots

For details on the simulation methods see

fitgauss for Gaussian random fields
fitgauss for linear models

If x-coordinates are not given, the function will check data for NAs and will perform imputing.

The function has many more options to tune the optimizer, see RFoptions for details.

If the model defines a Gaussian random field, the options for methods and submethods are currently "ml" and c("self", "plain", "sqrt.nr", "sd.inv", "internal"), respectively.

The result depends on the logical value of spConform. If TRUE, an S4 object is created. In case the model indicates a Gaussian random field, an RFfit object is created.

If spConform=FALSE, a list is returned. In case the model indicates a Gaussian random field, the details are given in fitgauss.

An important optional argument is boxcox which indicates a Box-Cox transformation; see boxcox in RFoptions and RFboxcox for details.
Instead of optim, other optimisers can be used, see RFfitOptimiser.
Several advanced options can be found in sections ‘General options’ and ‘fit’ of RFoptions.
In particular, boxcox, boxcox_lb, boxcox_ub allow Box-Cox transformation.
This function does not depend on the value of RFoptions()$PracticalRange. The function RFfit always uses the standard specification of the covariance model as given in RMmodel.

Burnham, K. P. and Anderson, D. R. (2002) Model selection and Multi-Model Inference: A Practical Information-Theoretic Approach. 2nd edition. New York: Springer.

RFfitOptimiser, RFlikelihood, RFratiotest, RMmodel, RandomFields, weather.

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




RFoptions(modus_operandi="sloppy")


#########################################################
## simulate some data first                            ## 
points <- 100
x <- runif(points, 0, 3)
y <- runif(points, 0, 3) ## random points in square [0, 3]^2
model <- RMgencauchy(alpha=1, beta=2)
d <- RFsimulate(model, x=x, y=y, grid=FALSE, n=100) #1000


#########################################################
## estimation; 'NA' means: "to be estimated"           ##
estmodel <- RMgencauchy(var=NA, scale=NA, alpha=NA, beta=2) +
            RMtrend(mean=NA)
RFfit(estmodel, data=d)


#########################################################
## coupling alpha and beta                             ##
estmodel <- RMgencauchy(var=NA, scale=NA, alpha=NA, beta=NA) + 
            RMtrend(NA)
RFfit(estmodel, data=d, transform = NA) ## just for information
trafo <- function(a) c(a[1], rep(a[2], 2))
fit <- RFfit(estmodel, data=d,
             transform = list(c(TRUE, TRUE, FALSE), trafo))
print(fit)
print(fit, full=TRUE)