psgp (version 0.3-19)

estimateParameters.psgp: Parameter estimation using a Projected Sequential Gaussian Process (PSGP)

Description

This overloads the estimateParameters routine from the intamap package for interpolation using the PSGP method.

Usage

estimateParameters(object, ...)

Arguments

object

a list object of Intamap type. Most arguments necessary for interpolation are passed through this object. See intamap-package for further description of the necessary content of this variable.

...

other parameters for the generic method, not used for this method

Author

Remi Barillec, Ben Ingram

Details

See psgp-package and learnParameters for further details.

See Also

learnParameters, estimateParameters, makePrediction, createIntamapObject

Examples

# load our favourite dataset
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")

# the following two steps are only needed if one wishes to
# include observation errors

# indicate which likelihood model should be used for each observation
# in this case we use a different model for each observation
nobs = length(meuse$value)          # Number of observations
meuse$oeid  <- seq(1:nobs)
  
# the variances for the error models are random in this example
# in real examples they will come from actual measurements 
# characteristics
meuse$oevar <- abs( rnorm( max(meuse$oeid) ) )

# set up intamap object:
obj = createIntamapObject(
  observations = meuse,
  predictionLocations = meuse.grid,
  targetCRS = "+init=epsg:3035",
  class = "psgp"    # Use PSGP for parameter estimation/interpolation
)

# do interpolation step:
obj = conformProjections(obj)
obj = estimateParameters(obj)