RandomFields (version 3.1.16)

RFboxcox: Linear part of RMmodel

Description

RFboxcox performs the Box-Cox transformation: $\frac{(x+\mu)^\lambda-1}{\lambda}$

Usage

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

Arguments

data

matrix or list of matrices.

boxcox

the one or two parameters of the box cox transformation, in the univariate case. If not given, the globally defined parameters are used, see Details. In the $m$-variate case boxcox should be a $2 \times m$ matrix.

vdim

the multivariate dimensionality of the field;

inverse

logical. Wether 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.

Value

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.

Details

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, RFempiricalvariogram. 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 parameters $\mu$ is set to $0$, which is the standard case.

References

For the likeilhood correction see

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

See Also

Bayesian, RMmodel, RFsimulate, RFlikelihood.

Examples

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)
)
data <- soil["moisture"]


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

## Assuming log-Gaussian Data
print(fit <- RFfit(model, data=data, loggaus=TRUE))

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