surf.gls: Fits a Trend Surface by Generalized Least-squares

In spatial: Functions for Kriging and Point Pattern Analysis

Fits a Trend Surface by Generalized Least-squares

Description

Fits a trend surface by generalized least-squares.

Usage

surf.gls(np, covmod, x, y, z, nx = 1000, ...)

Arguments

np	degree of polynomial surface
covmod	function to evaluate covariance or correlation function
x	x coordinates or a data frame with columns x, y, z
y	y coordinates
z	z coordinates. Will supersede x$z
nx	Number of bins for table of the covariance. Increasing adds accuracy, and increases size of the object.
...	parameters for covmod

Value

list with components

beta	the coefficients
x
y
z	and others for internal use only.

References

Ripley, B. D. (1981) Spatial Statistics. Wiley.

Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.

See Also

trmat, surf.ls, prmat, semat, expcov, gaucov, sphercov

Examples

library(MASS)  # for eqscplot
data(topo, package="MASS")
topo.kr <- surf.gls(2, expcov, topo, d=0.7)
trsurf <- trmat(topo.kr, 0, 6.5, 0, 6.5, 50)
eqscplot(trsurf, type = "n")
contour(trsurf, add = TRUE)

prsurf <- prmat(topo.kr, 0, 6.5, 0, 6.5, 50)
contour(prsurf, levels=seq(700, 925, 25))
sesurf <- semat(topo.kr, 0, 6.5, 0, 6.5, 30)
eqscplot(sesurf, type = "n")
contour(sesurf, levels = c(22, 25), add = TRUE)

spatial documentation built on July 26, 2023, 5:33 p.m.