estimateParameters {intamap} | R Documentation |
Automatic estimation of correlation structure parameters
Description
Function to estimate correlation structure parameters. The actual parameters depend on the method used.
Usage
## S3 method for class 'automap'
estimateParameters(object, ... )
## S3 method for class 'copula'
estimateParameters(object, ... )
## Default S3 method:
estimateParameters(object, ...)
## S3 method for class 'idw'
estimateParameters(object, ... )
## S3 method for class 'linearVariogram'
estimateParameters(object, ...)
## S3 method for class 'transGaussian'
estimateParameters(object, ... )
## S3 method for class 'yamamoto'
estimateParameters(object, ... )
Arguments
object |
an intamap object of the type described in |
... |
other arguments that will be passed to the requested interpolation method. See the individual methods for more information. Some parameters that are particular for some methods:
|
Details
The function estimateParameters
is a wrapper around different
methods for estimating correlation parameters to be used for the spatial
prediction method spatialPredict
.
Below are some details about and/or links to the different methods currently implemented
in the intamap-package
.
automap
It is possible but not necessary to estimate variogram parameters for this method. If
estimateParameters
is called with an object of class automap,autofitVariogram
will be called. Ifobject
already includes a variogram model whenspatialPredict
is called,krige
in thegstat
-package will be called directly. The user can submit an argumentmodel
with the model(s) to be fitted.copula
finding the best copula parameters using
copulaEstimation
default
a default method is not really implemented, this function is only created to give a sensible error message if the function is called with an object for which no method exist
idw
fits the best possible idw-power to the data set by brute force searching within the
idpRange
linearVariogram
this function just returns the original data, no parameter fitting is necessary for linear variogram kriging
transGaussian
Finding the best model parameters for transGaussian kriging (
krigeTg
). This means finding the bestlambda
for theboxcox
-transformation and the fitted variogram parameters for the transformed variable. Ifsignificant = TRUE
willlambda
only be estimated if the data show some deviation from normality, i.e., that at least one of the tests described underinterpolate
is TRUE. Note that transGaussian kriging is only possible for data with strictly positive values.yamamoto
a wrapper around
estimateParameters.automap
, only to assure that there is a method also for this class, difference toautomap
is more important inspatialPredict
It is also possible to add to the above methods with functionality from other packages, if wanted. You can also check which methods are available from other packages by calling
>methods(estimateParameters)
Value
a list object similar to object
, but extended with correlation parameters.
Author(s)
Jon Olav Skoien
References
Pebesma, E., Cornford, D., Dubois, G., Heuvelink, G.B.M., Hristopulos, D., Pilz, J., Stohlker, U., Morin, G., Skoien, J.O. INTAMAP: The design and implementation f an interoperable automated interpolation Web Service. Computers and Geosciences 37 (3), 2011.
See Also
createIntamapObject
, spatialPredict
, intamap-package
Examples
set.seed(13131)
# set up data:
data(meuse)
coordinates(meuse) = ~x+y
meuse$value = log(meuse$zinc)
data(meuse.grid)
gridded(meuse.grid) = ~x+y
proj4string(meuse) = CRS("+init=epsg:28992")
proj4string(meuse.grid) = CRS("+init=epsg:28992")
# set up intamap object:
idwObject = createIntamapObject(
observations = meuse,
formulaString=as.formula(zinc~1),
predictionLocations = meuse.grid,
class = "idw"
)
# run test:
checkSetup(idwObject)
# do interpolation steps:
idwObject = estimateParameters(idwObject, idpRange = seq(0.25,2.75,.25),
nfold=3) # faster
idwObject$inverseDistancePower