Description Usage Arguments Value Note Author(s) Examples
Function to generate isotropic covariance models, or add an isotropic covariance model to an existing isotropic model
1 2 3 4 5 |
modelname |
character vector, name of the covariance model, e.g. "exponential",
"spherical", "gauss". A call of |
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 |
add.covmodel |
object of the class |
x |
a covariance model generated by |
... |
further printing arguments |
an object of the class covmodel
that define a covariance model.
The names and parametrisation of the covariance model originate
from the CovarianceFct
in the RandomFields package. The
values of the arguments mev
, nugget
, variance
and scale
are by default = 0.
Please, be aware that you only can generate spatial isotropic covariance models, Time-Space models or so called (hypermodels) are not implemented.
Christoph Hofer christoph.hofer@alumni.ethz.ch
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | ## Not run:
# 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)
## End(Not run)
|
Loading required package: sp
Loading required package: spatialCovariance
The constrainedKriging package provides functions for efficient
computations of nonlinear spatial predictions with local change of support.
implemented covariance functions inside constrainedKriging
-------------------------------------------------------
covariance model | parameter for 2D
-------------------------------------------------------
* bessel | a > 0
* cauchy | a > 0
* cauchytbm | a = (0,2], b > 0
* circular | a = (0,2], b > 0
* constant | NULL
* cubic | NULL
* dampedcosine | a > 1
* exponential | NULL
* gauss | NULL
* spherical | NULL
* gencauchy | a = (0,2], b > 0
* gengneiting | a = 1, b >= 2.5 , a = 2, b >= 3.5, a = 3, b >= 4.5
* gneiting | NULL
* hyperbolic | a,b,c > 0 | a,c,> 0, b = 0 | a >= 0, c > 0, b < 0
* lgd1 | a in (0,0.5], b > 0
* nugget | NULL
* penta | NULL
* power | a > 1.5
* wave | NULL
* qexponential | a in [0,1]
* matern | a > 0
* whittle | a > 0
* stable | a in (0,2]
* gencauchy | a in (0,2], b > 0
-------------------------------------------------------
For more details please check the help page of the
function CovarianceFct in the RandomFields package.
-------------------------------------------------------
model name psill scale
1 mev 0 0
2 exponential 10 15
model name psill scale
1 mev 0.5 0
2 nugget 2.1 0
3 exponential 10 15
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