R: Linear part of 'RMmodel'

RFboxcox {RandomFields} | R Documentation

## Linear part of `RMmodel`

### Description

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

### 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.

### 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 μ is set to 0, which is the standard case.

### 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.

### Author(s)

Martin Schlather, schlather@math.uni-mannheim.de http://ms.math.uni-mannheim.de/de/publications/software

### 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))

    ## Not run:
    ## Assuming log-Gaussian Data
    print(fit <- RFfit(model, data=data, loggaus=TRUE))

    ## End(Not run)

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

[Package RandomFields version 3.1.50 Index]