Krigvar: Combinatorial variance computation.

In KRIG: Spatial Statistic with Kriging

Description

Computation of variance

Usage

Krigvar(KF, Gamma)

Arguments

KF	values of the kernel integral evaluations.
Gamma	Cube with integral results.

Value

Real value of sensitivity.

Author(s)

Pedro Guarderas [e-mail].

References

\insertRef

Kanova:2013KRIG \insertRefAronszajn:ThRKKRIG

Examples

library( KRIG ) 
options( stringsAsFactors = FALSE )
 
kernel_1<-function( x, y ) exp( -0.5*(x-y)^2)
kernel_2<-function( x, y ) exp( -0.7*(x-y)^2)
kernel_3<-function( x, y ) exp( -0.1*(x-y)^2)

Kernels<-data.frame( kernel = c( 'kernel_1', 'kernel_2', 'kernel_3' ), 
                     min = c( -1, -1, -2 ), 
                     max = c( 1, 1, 2 ),
                     n = c( 100, 100, 100 ) )
                     
n<-20
X<-matrix( c( seq( -1, 1, length.out = n ), 
              seq( -1, 1, length.out = n ),
              seq( -2, 2, length.out = n ) ), n, 3 )
              
KI<-list_integrate_kernel( Kernels, X )
GK<-Kanova( Kernels, KI, X )
    
f<-function( x ) x[1] + 30 * x[2] + 60 * x[3]
Func<-apply( X, 1, FUN = f )
    
KF<-solve( GK$Kanova + diag( 1e-8, n, n ), Func )
    
SbI<-NULL
for ( j in 1:3 ) {
  CB<-combn( 1:3, j )  
  for ( l in 1:ncol( CB ) ) {
    SbI<-c( SbI, Krigidx( KF, CB[,l], X, GK$Gamma ) )
    names(SbI)[length(SbI)]<-paste( 'C.', paste( CB[,l], collapse='.' ), sep = '' )
  }
}
  
Var<-Krigvar( KF, GK$Gamma )
    
SVar<-sum( SbI / Var )

KRIG documentation built on May 2, 2019, 5:55 a.m.