constrainedKriging (version 0.1.1)

covmodel: Create isotropic covariance model

Description

Function to generate isotropic covariance models, or add an isotropic covariance model to an existing isotropic model

Usage

covmodel(modelname, mev, nugget,variance, scale,
parameter, add.covmodel)
## S3 method for class 'covmodel':
print(x, \ldots)

Arguments

modelname

character vector, name of the covariance model, e.g. "exponential", "spherical", "gauss". A call of covmodel() without a function argument displays a table with all available models and their parameters. Check the CovarianceFct

mev

numeric value, variance of the measurement error

nugget

numeric value, variance of microstructure white noise process (range smaller than the data support)

variance

numeric value, partial sill of the variogram model

scale

numeric value, scale parameter of the variogram model

parameter

numeric vector of covariance parameters, missing for some model like nugget, spherical or gauss or

add.covmodel

object of the class covmodel that is added to the covariance model defined by modelname (see examples)

x

a covariance model generated by covmodel

...

further printing arguments

Value

• an object of the class covmodel that define a covariance model.

Examples

# table with all available covariance models and their
# parameters
covmodel()

# exponential model without a measurement error and without a nugget,
# partial sill = 10, scale  parameter = 15 
covmodel(modelname = "exponential", variance = 10, scale = 15)

# exponential model with a measurement error ( mev = 0.5) and a
# nugget (nugget = 2.1), exponential partial  sill (variance = 10)
# and scale parameter = 15
covmodel(modelname = "exponential", mev  = 0.5, nugget = 2.1,
variance = 10, scale = 15)