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 |
y | y coordinates |
z | z coordinates. Will supersede |
nx | Number of bins for table of the covariance. Increasing adds accuracy, and increases size of the object. |
... | parameters for |
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)
Copyright (©) 1999–2012 R Foundation for Statistical Computing.
Licensed under the GNU General Public License.