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ossfim 27ossfimKriging standard errors as function of grid spacing and block sizeDescriptionCalculate, for a given variogram model, ordinary block kriging standard errors as a function ofsampling spaces and block sizesUsageossfim(spacings = 1:5, block.sizes = 1:5, model, nmax = 25, debug = 0)Argumentsspacingsblock.sizesmodelnmaxdebugrange of grid (data) spacings to be usedrange of block sizes to be usedvariogram model, output of vgmset the kriging neighbourhood sizedebug level; set to 32 to see a lot of outputValuedata frame with columns spacing (the grid spacing), block.size (the block size), and kriging.se(block kriging standard error)NoteThe idea is old, simple, but still of value. If you want to map a variable with a given accuracy, youwill have to sample it. Suppose the variogram of the variable is known. Given a regular samplingscheme, the kriging standard error decreases when either (i) the data spacing is smaller, or (ii)predictions are made for larger blocks. This function helps quantifying this relationship. Ossfimprobably refers to “optimal sampling scheme for isarithmic mapping”.Author(s)Edzer J. PebesmaReferencesBurrough, P.A., R.A. McDonnell (1999) Principles of Geographical Information Systems. OxfordUniversity Press (e.g., figure 10.11 on page 261)Burgess, T.M., R. Webster, A.B. McBratney (1981) Optimal interpolation and isarithmic mappingof soil properties. IV Sampling strategy. The journal of soil science 32(4), 643-660.McBratney, A.B., R. Webster (1981) The design of optimal sampling schemes for local estimationand mapping of regionalized variables: 2 program and examples. Computers and Geosciences 7:335-365.read more on a simplified, web-based version on http://www.gstat.org/ossfim.html