R: REML Fit Direct Variogram Partial Sills to Data

fit.variogram.reml {gstat} | R Documentation

## REML Fit Direct Variogram Partial Sills to Data

### Description

Fit Variogram Sills to Data, using REML (only for direct variograms; not for cross variograms)

### Usage

    fit.variogram.reml(formula, locations, data, model, debug.level = 1, set, degree = 0)

### Arguments

`formula` | formula defining the response vector and (possible) regressors; in case of absence of regressors, use e.g. `z~1`
`locations` | spatial data locations; a formula with the coordinate variables in the right hand (dependent variable) side.
`data` | data frame where the names in formula and locations are to be found
`model` | variogram model to be fitted, output of `vgm`
`debug.level` | debug level; set to 65 to see the iteration trace and log likelihood
`set` | additional options that can be set; use `set=list(iter=100)` to set the max. number of iterations to 100.
`degree` | order of trend surface in the location, between 0 and 3

### Value

an object of class "variogramModel"; see fit.variogram

### Note

This implementation only uses REML fitting of sill parameters. For each iteration, an n x n matrix is inverted, with $n$ the number of observations, so for large data sets this method becomes demanding. I guess there is much more to likelihood variogram fitting in package `geoR`, and probably also in `nlme`.

### Author(s)

Edzer Pebesma

### References

Christensen, R. Linear models for multivariate, Time Series, and Spatial Data, Springer, NY, 1991.

Kitanidis, P., Minimum-Variance Quadratic Estimation of Covariances of Regionalized Variables, Mathematical Geology 17 (2), 195–208, 1985

### See Also

fit.variogram,

### Examples

    library(sp)
    data(meuse)
    fit.variogram.reml(log(zinc)~1, ~x+y, meuse, model = vgm(1, "Sph", 900,1))

[Package gstat version 2.0-3 Index]