intamap (version 1.3-4)

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, ... , idpRange = seq(0.1, 2.9, 0.1), nfolds = 5)
## S3 method for class 'linearVariogram':
estimateParameters(object, ...)## S3 method for class 'transGaussian':
estimateParameters(object, lambda, significant = TRUE, ... )
## 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

idpRange

range of idp (inverse distance weighting power) values over which to optimize mse

nfolds

number of folds in n-fold cross validation

lambda

lambda parameter for boxcox-transformation

significant

logical; if TRUE only transform if any of the four tests described under interpolate are TRUE.

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