simgc: Simulate Geostatistical Data from Gaussian Copula Model at...

In gcKrig: Analysis of Geostatistical Count Data using Gaussian Copulas

Simulate Geostatistical Data from Gaussian Copula Model at Given Locations

Description

Simulate geostatistical data from Gaussian copula model at given locations. This function can simulate multiple datasets simultaneously.

Usage

simgc(locs, sim.n = 1, marginal, corr, longlat = FALSE)

Arguments

locs	a numeric matrix or data frame of n-D points with row denoting points. First column is x or longitude, second column is y or latitude. The number of locations is equal to the number of rows.
sim.n	the number of simulation samples required.
marginal	an object of class marginal.gc specifying the marginal distribution.
corr	an object of class corr.gc specifying the correlation function.
longlat	if FALSE, use Euclidean distance, if TRUE use great circle distance. Default is FALSE.

Value

A list of two elements:

data	a numeric matrix with each row denoting a simulated data.
locs	the location of the simulated data, same as the input locs.

Author(s)

Zifei Han [e-mail]

Examples

grid <- seq(0.05, 0.95, by = 0.1)
xloc <- expand.grid(x = grid, y = grid)[,1]
yloc <- expand.grid(x = grid, y = grid)[,2]
set.seed(12345)
sim1 <- simgc(locs = cbind(xloc,yloc), sim.n = 10, marginal = negbin.gc(mu = 5, od = 1),
              corr = matern.gc(range = 0.3, kappa = 0.5, nugget = 0.1))
#plot(sim1, index = 1)

gcKrig documentation built on July 3, 2022, 1:05 a.m.