RFpseudovariogram: Pseudovariogram
In RandomFields: Simulation and Analysis of Random Fields

Description Usage Arguments Details Value References See Also Examples

Calculates the theoretical and empirical Pseudovariogram.

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RFpseudovariogram(model, x, y=NULL, z = NULL, T=NULL, grid, params, distances,
            dim, ..., data, bin=NULL, phi=NULL, theta = NULL,
            deltaT = NULL, vdim=NULL)

`model,params`	\argModel
`x`	\argX
`y,z`	\argYz
`T`	\argT
`grid`	\argGrid
`distances,dim`	\argDistances
`...`	\argDots
`data`	\argData
`bin`	\argBin
`phi`	\argPhi
`theta`	\argTheta
`deltaT`	\argDeltaT
`vdim`	\argVdim

RFpseudovariogram computes the empirical pseudovariogram for given (multivariate) spatial data.

The spatial coordinates x, y, z should be vectors. For random fields of spatial dimension d > 3 write all vectors as columns of matrix x. In this case do neither use y, nor z and write the columns in gridtriple notation.

If the data is spatially located on a grid a fast algorithm based on the fast Fourier transformed (fft) will be used. As advanced option the calculation method can also be changed for grid data (see RFoptions.)

an objects of class RFempVariog.

Gelfand, A. E., Diggle, P., Fuentes, M. and Guttorp, P. (eds.) (2010) Handbook of Spatial Statistics. Boca Raton: Chapman & Hall/CRL.

Stein, M. L. (1999) Interpolation of Spatial Data. New York: Springer-Verlag

RMstable, RMmodel, RFsimulate, RFfit, RFcov, RFmadogram. RFvariogram,

RFoptions(seed=0) ## *ANY* simulation will have the random seed 0; set
##                   RFoptions(seed=NA) to make them all random again


model <- RMbiwm(nudiag=c(1, 2), nured=1, rhored=1, cdiag=c(1, 5), 
                s=c(1, 1, 2))
x <- seq(0, 20, 0.1)
z <- RFsimulate(model, x=x, y=x, n=2)
emp.vario <- RFpseudovariogram(data=z)
plot(emp.vario, model=model)