intamap (version 1.3-21)

estimateParameters: 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, ... )
## S3 method for class 'default':
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 intamap-package

...

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: [object Object],[object Object],[object Object],[object Object]

Value

• a list object similar to object, but extended with correlation parameters.

synopsis

estimateParameters(object, ...)

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. [object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object] It is also possible to add to the above methods with functionality from other packages, if wanted. See description on http://www.intamap.org/newMethods.php You can also check which methods are available from other packages by calling >methods(estimateParameters)

References

http://www.intamap.org/

See Also

createIntamapObject, spatialPredict, intamap-package

Examples

library(intamap)

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