aniso: Fitting anisotropic spatial Gaussian process models

In deform: Spatial Deformation and Dimension Expansion Gaussian Processes

Fitting anisotropic spatial Gaussian process models

Description

Function aniso fits a conventional 2-dimensional anisotropic Gaussian process, i.e. just with scalings in the x and y coordinates.

Usage

aniso(x, z, n, correlation = FALSE, cosine = FALSE, standardise = "together")

Arguments

x	a 2-column matrix comprising x and y coordinates column-wise, respectively, or a list; see Details for the latter
z	a variance-covariance matrix
n	an integer number of data
correlation	a logical defining whether z should be assumed to be a correlation matrix; defaults to FALSE
cosine	a logical defining whether the powered exponential covariance function should be multiplied by the cosine of scaled distances, i.e. giving a damped oscillation; defaults to FALSE
standardise	a character string that governs whether dimensions are scaled by a common ("together") or dimension-specific factor; defaults to "together"

Details

If x is a list, then it wants elements "x", "z" and "n" as described above.

Value

An object of class deform and then of class anisotropic

References

Sampson, P. D. and Guttorp, P. (1992) Nonparametric Estimation of Nonstationary Spatial Covariance Structure, Journal of the American Statistical Association, 87:417, 108-119, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/01621459.1992.10475181")}'

Examples

data(solar)
aniso(solar$x, solar$z, solar$n) 
# equivalent to aniso(solar)

deform documentation built on Oct. 19, 2023, 9:08 a.m.