# RMmodel

##### Covariance and Variogram Models in RandomFields

Summary of implemented covariance and variogram models

##### Details

To generate a covariance or variogram model for use within

`...`

can take model specific parameters. %Parameter %corresponding to specific covariance model`var`

is the optional variance parameter$v$,`scale`

the optional scale parameter$s$,`Aniso`

the optional anisotropy matrix$A$, and`proj`

is the optional projection vector which defines a diagonal matrix of zeros and ones and`proj`

gives the positions of the ones (integer values).

**Basic stationary and isotropic models**

**Variogram models (stationary increments/intrinsically stationary)**

**Basic Operations**

**Basic models for mixed effect modelling**

**Trend**

**See RMmodelsAdvanced for many more, advanced models.**

##### References

- Chiles, J.-P. and Delfiner, P. (1999)
*Geostatistics. Modeling Spatial Uncertainty.*New York: Wiley. % \item Gneiting, T. and Schlather, M. (2004) % Statistical modeling with covariance functions. % \emph{In preparation.} - Schlather, M. (1999)
*An introduction to positive definite functions and to unconditional simulation of random fields.*Technical report ST 99-10, Dept. of Maths and Statistics, Lancaster University. - Schlather, M. (2011) Construction of covariance functions and
unconditional simulation of random fields. In Porcu, E., Montero, J.M.
and Schlather, M.,
*Space-Time Processes and Challenges Related to Environmental Problems.*New York: Springer. - Yaglom, A.M. (1987)
*Correlation Theory of Stationary and Related Random Functions I, Basic Results.*New York: Springer. - Wackernagel, H. (2003)
*Multivariate Geostatistics.*Berlin: Springer, 3nd edition.

##### See Also

##### Examples

```
set.seed(0)
RFgetModelNames(type="positive definite", domain="single variable",
isotropy="isotropic", operator=FALSE)
```

*Documentation reproduced from package RandomFields, version 3.0.5, License: GPL (>= 3)*