Finds Universal Kriging weights using X,
the $n \times k$
design matrix for the regression coefficients of the observed
data,
V, the (positive definite) covariance matrix of the
observed responses, Xp, the $np \times k$ design matrix
of the responses to be predicted, Vp, the
$np \times np$
covariance matrix of the responses to be predicted, 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
pweights.uk(X, V, Xp, Vp, Vop)
Arguments
X
The design matrix of the observed data.
The size is $n \times k$
V
The covariance matrix of the observed responses.
The size is $n times n$.
Xp
The design matrix of the responses to be predicted.
The size is $np \times k$
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 w, the $np \times n$ matrix containing the kriging weights used to calculate the predicted values.