SpatialTools (version 0.3.2)

mspe.uk: Calculates mean-square prediction error of Universal Kriging predictions

Description

Calculates mean-square prediction error of Universal Kriging predictions using w, the $n \times np$ matrix of kriging weights, V, the (positive definite) covariance matrix of the observed responses, Vp, the (positive definite) covariance matrix of the predicted responses,and Vop, the $n x np$ matrix of covariances between the observed responses and the responses to be predicted. Uses Armadillo C++ template via RcppArmadillo to perform most of the operations.

Usage

mspe.uk(w, V, Vp, Vop)

Arguments

w

The matrix of kriging weights for the predicted responses. The size is $n \times np$

V

The covariance matrix of the observed responses. The size is $n times n$.

Vp

The covariance matrix of the responses to be predicted. The size is $np times np$.

Vop

The cross-covariance between the observed responses and the responses to be predicted. The size is $n \times np$

Value

• The function returns a list containing mspe, a matrix of size $n \times 1$ containing the mean-square prediction error of the predicted responses.

See Also

krige.uk, weights.uk

Examples

data(toydata)
	X <- toydata$X
	V <- toydata$V
	Xp <- toydata$Xp
	Vp <- toydata$Vp
	Vop <- toydata$Vop
	w <- pweights.uk(X, V, Xp, Vp, Vop)
	mspe.uk(w, V, Vp, Vop)