spatial (version 7.3-5)

surf.gls: 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)