geostatSim: Simulate geostatistical data on set of given locations

In highamm/sptotal: Predicting Totals and Weighted Sums from Spatial Data

Simulate geostatistical data on set of given locations

Description

Spatially correlated data are simulated assuming a multivariate normal random error vector. For simplicity, only "Exponential" and "Spherical" simulation options are given here.

Usage

geostatSim(
  loc.data,
  xcol = "x",
  ycol = "y",
  parsil = 1,
  range = 1,
  nugget = 0,
  minorp = 1,
  rotate = 90,
  extrap = NULL,
  CorModel = "Exponential"
)

Arguments

loc.data	data.frame with x- and y-coordinates of locations for simulated data
xcol	name of the column in loc.data with x-coordinates, default is "x"
ycol	name of the column loc.data with y-coordinates, default is "y"
parsil	partial sill of autocorrelation model, default = 1
range	range of autocorrelation model, default = 1
nugget	range of autocorrelation model, default = 0
minorp	proportion of range in x direction to that of y direction for unrotated anisotropic model, default = 1
rotate	rotation of anisotropic axes, default = 90
extrap	extra covariance parameter
CorModel	autocorrelation model, default = "Exponential". Other possibilities are "Spherical".

Value

data.frame of three columns, the original location data appended with a 3rd column of simulated geostatistical data

Author(s)

Jay Ver Hoef

Examples

locations <- expand.grid(1:10, 1:10)
geostatSim(locations, xcol = "Var1", ycol = "Var2",
parsil = 4, range = 20, nugget = 1, CorModel = "Exponential")

highamm/sptotal documentation built on Aug. 12, 2024, 2:04 a.m.