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.