disc.sb: Discretization nodes of a Shapiro-Botha variogram model

In npsp: Nonparametric Spatial Statistics

Discretization nodes of a Shapiro-Botha variogram model

Description

Computes the discretization nodes of a ‘nonparametric’ extended Shapiro-Botha variogram model, following Gorsich and Genton (2004), as the scaled roots of Bessel functions.

Usage

disc.sb(nx, dk = 0, rmax = 1)

Arguments

nx	number of discretization nodes.
dk	dimension of the kappa function (dk >= 1, see Details below).
rmax	maximum lag considered.

Details

If dk >= 1, the nodes are computed as:

x_i = q_i/rmax; i = 1,\ldots, nx,

where q_i are the first n roots of J_{(d-2)/2}, J_p is the Bessel function of order p and rmax is the maximum lag considered. The computation of the zeros of the Bessel function is done using the efficient algorithm developed by Ball (2000).

If dk == 0 (corresponding to a model valid in any spatial dimension), the nodes are computed so the gaussian variogram models involved have practical ranges:

r_i = 2 ( 1 + (i-1) ) rmax/nx; i = 1,\ldots, nx.

Value

A vector with the discretization nodes.

References

Ball, J.S. (2000) Automatic computation of zeros of Bessel functions and other special functions. SIAM Journal on Scientific Computing, 21, 1458-1464.

Gorsich, D.J. and Genton, M.G. (2004) On the discretization of nonparametric covariogram estimators. Statistics and Computing, 14, 99-108.

See Also

kappasb, fitsvar.sb.iso.

Examples

disc.sb( 12, 1, 1.0)

nx <- 1
dk <- 0
x <- disc.sb(nx, dk, 1.0)
h <- seq(0, 1, length = 100)
plot(h, kappasb(x * h, 0), type="l", ylim = c(0, 1))
abline(h = 0.05, lty = 2)

npsp documentation built on May 4, 2023, 1:07 a.m.