RFmadogram: Empirical (Cross-)Madogram
In RandomFields: Simulation and Analysis of Random Fields

Description Usage Arguments Details Value Author(s) References See Also Examples

Calculates the empirical (cross-)madogram. The empirical (cross-)madogram of two random fields X and Y is given by

γ(r):=1/N(r) ∑_{(t_{i},t_{j})|t_{i,j}=r} |(X(t_{i})-X(t_{j}))||(Y(t_{i})-Y(t_{j}))|

where t_{i,j}:=t_{i}-t_{j}, and where N(r) denotes the number of pairs of data points with distancevector t_{i,j}=r.

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RFmadogram(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

RFmadogram computes the empirical cross-madogram 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.)

It is also possible to use RFmadogram to calculate the pseudomadogram (see RFoptions).

RFmadogram returns objects of class RFempVariog.

Jonas Auel; Sebastian Engelke; Johannes Martini; \martin

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, RFpseudomadogram. RFvariogram.

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

n <- 1 ## use n <- 2 for better results

## isotropic model
model <- RMexp()
x <- seq(0, 10, 0.02)
z <- RFsimulate(model, x=x, n=n)
emp.vario <- RFmadogram(data=z)
plot(emp.vario)


## anisotropic model
model <- RMexp(Aniso=cbind(c(2,1), c(1,1)))
x <- seq(0, 10, 0.05)
z <- RFsimulate(model, x=x, y=x, n=n)
emp.vario <- RFmadogram(data=z, phi=4)
plot(emp.vario)


## space-time model
model <- RMnsst(phi=RMexp(), psi=RMfbm(alpha=1), delta=2)
x <- seq(0, 10, 0.05)
T <- c(0, 0.1, 100)
z <- RFsimulate(x=x, T=T, model=model, n=n)
emp.vario <- RFmadogram(data=z, deltaT=c(10, 1))
plot(emp.vario, nmax.T=3)


## multivariate model
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=n)
emp.vario <- RFmadogram(data=z)
plot(emp.vario)


## multivariate and anisotropic model
model <- RMbiwm(A=matrix(c(1,1,1,2), nc=2),
                nudiag=c(0.5,2), s=c(3, 1, 2), c=c(1, 0, 1))
x <- seq(0, 20, 0.1)
dta <- RFsimulate(model, x, x, n=n)
ev <- RFmadogram(data=dta, phi=4)
plot(ev, boundaries=FALSE)