The simplest function of this method is to convert the
a.wght value into a list that has the length of the number of levels.
If only a scalar a.wght value is supplied then the default method
just repeats this for each level.
The function LKrigSetupAwghtObject
uses the
a.wghtObject component in the LKinfo object to fill
in a.wght parameters for the different
levels. This is convenient because the lattice locations are different at each level. The parameters are filled in at level, Level
according to
latticeLocations<- make.surface.grid(
object$latticeInfo$grid[[Level]])
a.wght<- predict( object$a.wghtObject, latticeLocations )
here the predict function is whatever is supplied according to the
class for a.wghtObject. Note that since the returned set of
parameters will be in the format used internally a.wght here will be
a list with each component being a matrix. Number of rows are each to
the number of lattice points (or basis functions) at that level.
This is easier implement that it may seem and see the examples in nonstationaryModels.
The attribute
fastNormalize
(either TRUE or FALSE) is attached to this
list to indicate how the marginal variance of the process should be
found.
LKinfo<- LKrigSetup( x,LKGeometry="LKInterval", alpha=c( 1,.2,.01),
nlevel=3, a.wght=4.5, NC= 3)
LKrigSetupAwght( LKinfo)
[[1]]
[1] 4.5[[2]]
[1] 4.5
[[3]]
[1] 4.5
attr(,"fastNormalize")
[1] FALSE
Currently the only geometry with fastNormalization being TRUE is for a
rectangular domain.
For the LKRectangle geometry, however, more complicated
anisotropic and non-stationary a.wght specifications are possible.
See LKrig
for details. Also in the case that the
fastNormalization is TRUE for rectangles several more attributes are added to the a.wght
list that precompute some matrices of the SAR.