Krig: Kriging computation.

In KRIG: Spatial Statistic with Kriging

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.

Author(s)

Pedro Guarderas [e-mail].

Examples

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' )

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