Download Package `gstat`

8
fit.variogram.reml
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×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
data(meuse)
fit.variogram.reml(log(zinc)~1, ~x+y, meuse, model = vgm(1, "Sph", 900,1))