autoKrige.cv: Automatic cross-validation

Description

Uses autofitVariogram to fit a variogram model to the data and then calls krige.cv to perform cross-validation.

Usage

autoKrige.cv(formula, 
	     input_data, 
	     data_variogram = input_data,
	     model = c("Sph", "Exp", "Gau", "Ste"), 
	     kappa = c(0.05, seq(0.2, 2, 0.1), 5, 10), 
	     fix.values = c(NA,NA,NA), 
	     verbose = c(FALSE, interactive()), 
	     GLS.model = NA,
         start_vals = c(NA,NA,NA),
         miscFitOptions = list(),
	     ...)

Value

autoKrige.cv returns an object of class autoKrige.cv. This is a list containing one object of class SpatialPointsDataFrame with the results of the cross-validation, see krige.cv for more details. The attribute name is krige.cv_output.

Arguments

formula: formula that defines the dependent variable as a linear model of independent variables; suppose the dependent variable has name 'z', for ordinary and simple kriging use the formula 'z~1'; for simple kriging also define 'beta' (see below); for universal kriging, suppose 'z' is linearly dependent on 'x' and 'y', use the formula 'z~x+y'.
input_data: An object of the SpatialPointsDataFrame-class containing the data to be interpolated.
data_variogram: An optional way to provide a different dataset for the building of the variogram.
model: List of models that will be tested during automatic variogram fitting.
kappa: List of values for the smoothing parameter of the Matern model that will be tested during automatic variogram fitting.
fix.values: Can be used to fix a variogram parameter to a certain value. It consists of a list with a length of three. The items describe the fixed value for the nugget, range and sill respectively. Setting the value to NA means that the value is not fixed. Is passed on to autofitVariogram.
verbose: vector of 2 logicals. The first element sets the verbosity of autofitVariogram, see its documentation for more information. The second element sets the verbosity level of krige.cv, see its documentation for more information.
GLS.model: If a variogram model is passed on through this parameter a Generalized Least Squares sample variogram is calculated.
start_vals: Can be used to give the starting values for the variogram fitting. The items describe the fixed value for the nugget, range and sill respectively. They need to be given in that order. Setting the value to NA means that the value will be automatically chosen.
miscFitOptions: Additional options to set the behavior of autofitVariogram. For details see the documentation of autofitVariogram.
...: arguments passed to krige.cv

Author

Paul Hiemstra, paul@numbertheory.nl

Examples

Run this code

library(sp)
data(meuse)
coordinates(meuse) = ~x+y
data(meuse.grid)
gridded(meuse.grid) = ~x+y

kr.cv = autoKrige.cv(log(zinc)~1, meuse, model = c("Exp"), nfold = 10)
kr_dist.cv = autoKrige.cv(log(zinc)~sqrt(dist), meuse, 
       model = c("Exp"), nfold = 10)
kr_dist_ffreq.cv = autoKrige.cv(log(zinc)~sqrt(dist)+ffreq, 
       meuse, model = c("Exp"), nfold = 10)