RFboxcox: Linear part of 'RMmodel'
In RandomFields: Simulation and Analysis of Random Fields

Description Usage Arguments Details Value References See Also Examples

RFboxcox performs the Box-Cox transformation: \frac{(x+μ)^λ-1}{λ}

1	RFboxcox(data, boxcox, vdim = 1, inverse=FALSE, ignore.na=FALSE)

`data`	matrix or list of matrices.
`boxcox`	the one or two parameters (λ, μ) of the box cox transformation, in the univariate case; if μ is not given, then μ is set to 0. If not given, the globally defined parameters are used, see Details. In the m-variate case `boxcox` should be a 2 \times m matrix. If λ =∞ then no transformation is performed.
`vdim`	the multivariate dimensionality of the field;
`inverse`	logical. Whether the inverse transformation should be performed.
`ignore.na`	logical. If `FALSE` an error message is returned if any value of `boxcox` is `NA`. Otherwise the data are returned without being transformed.

The Box-Cox transfomation boxcox can be set globally through RFoptions. If it is set globally the transformation applies in the Gaussian case to RFfit, RFsimulate, RFinterpolate, RFvariogram. Always first, the Box-Cox transformation is applied to the data. Then the command is performed. The result is back-transformed before returned.

If the first value of the transformation is Inf no transformation is performed (and is identical to boxcox = c(1,0)). If boxcox has length 1, then the transformation parameter μ is set to 0, which is the standard case.

RFboxcox returns a list of three components, Y, X, vdim returning the deterministic trend, the design matrix, and the multivariability, respectively. If set is positive, Y and X contain the values for the set-th set of coordinates. Else, Y and X are both lists containing the values for all the sets.

For the likelihood correction see

Konishi, S., and Kitagawa, G. (2008) Information criteria and statistical modeling. Springer Science & Business Media. Section 4.9.

Bayesian, RMmodel, RFsimulate, RFlikelihood.

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

data(soil)
str(soil)
soil <- RFspatialPointsDataFrame(
 coords = soil[ , c("x.coord", "y.coord")],
 data = soil[ , c("moisture", "NO3.N", "Total.N", "NH4.N", "DOC", "N20N")],
 RFparams=list(vdim=6, n=1)
)
dta <- soil["moisture"]


model <- ~1 + RMplus(RMwhittle(scale=NA, var=NA, nu=NA), RMnugget(var=NA))



## main Parameter in the Box Cox transformation to be estimated
print(fit <- RFfit(model, data=dta, boxcox=NA))