R: Fits a Trend Surface by Generalized Least-squares

surf.gls {spatial} | R Documentation

## 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)

[Package spatial version 7.3-12 Index]