Description Usage Arguments Details Value Note References See Also Examples
RMgauss
is a stationary isotropic covariance model.
The corresponding covariance function only depends on the distance
r ≥ 0 between two points and is given by
C(r)=e^{r^2}.
1 
var,scale,Aniso,proj 
optional arguments; same meaning for any

This model is called Gaussian because of the functional similarity of the spectral density of a process with that covariance function to the Gaussian probability density function.
The Gaussian model has an infinitely differentiable covariance function. This smoothness is artificial. Furthermore, this often leads to singular matrices and therefore numerically instable procedures (cf. Stein, M. L. (1999), p. 29).
The Gaussian model is included in the symmetric stable class (see
RMstable
) for the choice alpha = 2.
RMgauss
returns an object of class
RMmodel
.
The use of RMgauss
is questionable from both a theoretical
(analytical paths) and a practical point of view (e.g. speed of
algorithms).
Instead, RMgneiting
should be used.
Gelfand, A. E., Diggle, P., Fuentes, M. and Guttorp, P. (eds.) (2010) Handbook of Spatial Statistics. Boca Raton: Chapman & Hall/CRL.
Stein, M. L. (1999) Interpolation of Spatial Data. New York: SpringerVerlag
RMstable
and RMmatern
for generalizations;
RMmodel
,
RFsimulate
,
RFfit
.
Do not mix up with RPgauss
or RRgauss
.
1 2 3 4 5 6 7 
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.