Linear part of RMmodel
RFboxcox performs the Box-Cox transformation:
\frac{(x+μ)^λ-1}{λ}
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 |
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.
Martin Schlather, schlather@math.uni-mannheim.de, https://www.wim.uni-mannheim.de/schlather/
For the likelihood correction see
Konishi, S., and Kitagawa, G. (2008) Information criteria and statistical modeling. Springer Science & Business Media. Section 4.9.
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))Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.