KRIG (version 0.1.0)

Krig: Kriging computation.

Description

Computes the kriging linear estimator for different types of kriging models.

Usage

Krig(Z, K, k, G, g, type = "ordinary", cinv = "syminv")

Arguments

Z

Observed values of the spatial process.

K

Covariance matrix computed for the position \(X\) where the spatial process \(Z\) was observed.

k

Covariance matrix computed for the position \(X\) where the spatial process \(Z\) was observed and the position \(Y\) where the spatial process \(Z\) will be predicted.

G

When universal kriging will be computed, this matrix represents the values of the of the functions representing the mean of the process \(Z\), evaluated in the spatial points \(X\) where the spatial process was first observed.

g

When universal kriging will be computed, this matrix represents the evaluation of the functions representing the mean over the new position points \(Y\) where the spatial process \(Z\) will be predicted.

type

Type of kriging model, possible values are: simple, ordinary, universal.

cinv

Specifies how the inverse of the covariance matrix \(K\) will be computed. Possible values are: syminv = symmetric matrix inverse computation, inv = usual armadillo inverse computation, cholinv = Cholesky based inverse computation, ginv = given inverse not necessary to compute inverse at all.

Value

Depending of the type of analysis the list of results change.

Z

New estimated values for Z.

L

Linear coefficients determined by kriging.

J

Inverse of the covariance matrix.

tau

Factor computed in the ordinary and universal kriging.

alpha

Factor computed in the ordinary kriging.

A

Factor computed in the universal kriging.

Examples

# NOT RUN {
library( KRIG )
vignette( topic = 'simple_kriging', package = 'KRIG' )
vignette( topic = 'ordinary_kriging', package = 'KRIG' )
vignette( topic = 'universal_kriging', package = 'KRIG' )
vignette( topic = 'copper_mining_2d', package = 'KRIG' )
# }