RMmodel: Covariance and Variogram Models in 'RandomFields' (RM...

Description Details Author(s) References See Also Examples

Description

Summary of implemented covariance and variogram models

Details

To generate a covariance or variogram model for use within RandomFields, calls of the form

RM_name_(..., var, scale, Aniso, proj)

can be used, where _name_ has to be replaced by a valid model name.

With φ denoting the original model, the transformed model is C(h) = v * φ(A*h/s). See RMS for more details.

RM_name_ must be a function of class RMmodelgenerator. The return value of all functions RM_name_ is of class RMmodel.

The following models are available (cf. RFgetModelNames):

Basic stationary and isotropic models

RMcauchy Cauchy family
RMexp exponential model
RMgencauchy generalized Cauchy family
RMgauss Gaussian model
RMgneiting differentiable model with compact support
RMmatern Whittle-Matern model
RMnugget nugget effect model
RMspheric spherical model
RMstable symmetric stable family or powered exponential model
RMwhittle Whittle-Matern model, alternative parametrization

Variogram models (stationary increments/intrinsically stationary)

RMfbm fractal Brownian motion

Basic Operations

RMmult, * product of covariance models
RMplus, + sum of covariance models or variograms

Others

RMtrend trend
RMangle defines a 2x2 anisotropy matrix by rotation and stretch arguments.

Author(s)

Alexander Malinowski; \martin

References

See Also

RM for an overview over more advanced classes of models
RC, RF, RP, RR, R., RFcov, RFformula, RMmodelsAdvanced, RMmodelsAuxiliary, trend modelling

Examples

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RFoptions(seed=0) ## *ANY* simulation will have the random seed 0; set
##                   RFoptions(seed=NA) to make them all random again

## an example of a simple model
model <- RMexp(var=1.6, scale=0.5) + RMnugget(var=0) #exponential + nugget
plot(model)

RandomFields documentation built on Feb. 6, 2020, 5:13 p.m.