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 (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.
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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