Description Usage Arguments Details Value References See Also Examples

`RFboxcox`

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

1 |

`data` |
matrix or list of matrices. |

`boxcox` |
the one or two parameters |

`vdim` |
the multivariate dimensionality of the field; |

`inverse` |
logical. Whether the inverse transformation should be performed. |

`ignore.na` |
logical. If |

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`

.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | ```
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))
``` |

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