Description Usage Arguments Details Value Author(s) References See Also Examples
Function to estimate correlation structure parameters. The actual parameters depend on the method used.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | ## 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, ... )
|
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:
|
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.
If object
already includes a variogram model when
spatialPredict
is called,
krige
in the gstat
-package will be called directly.
The user can submit an argument model
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 best lambda
for
the boxcox
-transformation and the fitted variogram
parameters for the transformed variable. If significant = TRUE
will lambda
only be estimated
if the data show some deviation from normality, i.e., that at least one
of the tests described under interpolate
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 to automap
is more important in spatialPredict
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
1 |
a list object similar to object
, but extended with correlation parameters.
Jon Olav Skoien
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.
createIntamapObject
, spatialPredict
, intamap-package
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 | 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
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