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TurningBandsSpherical: spherical-covariance Gaussian field by turning bands with Matheron's dilution line process. DlaAggregate: diffusion-limited aggregation on the square lattice. MovingLeastSquares: MLS approximation with Gaussian weights. DeformationFieldSimulate: Sampson-Guttorp deformation field. AnnealingSimulation: simulated annealing matching a histogram and a variogram. Philox draws; identical to the Python arm morie.fn.geosimx.

Usage

TurningBandsSpherical(
  coords,
  sill = 1,
  range_ = 1,
  n_bands = 100L,
  lam = NULL,
  seed = 1
)

DlaAggregate(n_particles, seed = 1)

MovingLeastSquares(points, values, targets, h, degree = 1L)

DeformationFieldSimulate(coords, deformed, model, nsim = 1L, seed = 1)

AnnealingSimulation(
  nx,
  ny,
  values,
  model,
  lags,
  n_iter = 5000L,
  t0 = 1,
  cooling = 0.001,
  seed = 1
)

Arguments

coords

Matrix of coordinates (two or three columns).

sill, range_

Sill and range.

n_bands

Number of bands.

lam

Poisson intensity of the dilution (default 50 / range).

seed

Philox seed.

n_particles

Number of walkers.

points, values, targets

Data points, their values and target points.

h

Bandwidth.

degree

Polynomial degree (1 or 2).

deformed

Matrix of deformed coordinates.

model

Covariance model (KrigingCovariance).

nsim

Number of realisations.

nx, ny

Grid size.

lags

Matrix of lag vectors (dx, dy).

n_iter, t0, cooling

Annealing iterations, initial temperature and cooling rate.

Value

List.

References

Lantuejoul, C. (2002). Geostatistical Simulation: Models and Algorithms. Springer.

Witten, T. A. and Sander, L. M. (1981). Diffusion-limited aggregation, a kinetic critical phenomenon. Physical Review Letters 47, 1400-1403.

Lancaster, P. and Salkauskas, K. (1981). Surfaces generated by moving least squares methods. Mathematics of Computation 37, 141-158.

Sampson, P. D. and Guttorp, P. (1992). Nonparametric estimation of nonstationary spatial covariance structure. JASA 87, 108-119.

Deutsch, C. V. and Journel, A. G. (1998). GSLIB, 2nd edn. Oxford University Press.

Examples

P <- rbind(c(0, 0), c(1, 0), c(0, 1), c(1, 1), c(0.5, 0.5))
MovingLeastSquares(P, 1 + 2 * P[, 1] - P[, 2], rbind(c(0.3, 0.7)), 0.8)$prediction
#> [1] 0.9