NSconvo_sim: Simulate data from the nonstationary model.

In markdrisser/convoSPAT: Convolution-Based Nonstationary Spatial Modeling

Description

NSconvo_sim simulates data from the nonstationary model, given mixture component kernel matrices. The function requires either a mixture component kernel object, from the function f.mc.kernels(), or a direct specification of the mixture component locations and mixture component kernels.

Usage

NSconvo_sim(grid = TRUE, y.min = 0, y.max = 5, x.min = 0, x.max = 5,
  N.obs = 20^2, sim.locations = NULL, mc.kernels.obj = NULL,
  mc.kernels = NULL, mc.locations = NULL, lambda.w = NULL, tausq = 0.1,
  sigmasq = 1, beta.coefs = 4, kappa = NULL, covariates = rep(1, N.obs),
  cov.model = "exponential")

Arguments

grid	Logical; indicates of the simulated data should fall on a grid (TRUE) or not (FALSE).
y.min	Lower bound for the y-coordinate axis.
y.max	Upper bound for the y-coordinate axis.
x.min	Lower bound for the y-coordinate axis.
x.max	Upper bound for the y-coordinate axis.
N.obs	Number of simulated data values.
sim.locations	Optional N.obs x 2 matrix; allows the user to specify the locations of the simulated data.
mc.kernels.obj	Object from the f_mc_kernels function.
mc.kernels	Optional specification of mixture component kernel matrices.
mc.locations	Optional specification of mixture component locations.
lambda.w	Scalar; tuning parameter for the weight function.
tausq	Scalar; true nugget variance.
sigmasq	Scalar; true process variance.
beta.coefs	Vector of true regression coefficients. Length must match the number of columns in covariates.
kappa	Scalar; true smoothness.
covariates	Matrix with N.obs rows, corresponding to covariate information for each of the simualted values.
cov.model	A string specifying the correlation function. Options available in this package are: "exponential", "cauchy", "matern", "circular", "cubic", "gaussian", "spherical", and "wave". See cov.spatial for further documentation.

Value

A list with the following components:

sim.locations	Matrix of locations for the simulated values.
mc.locations	Mixture component locations used for the simulated data.
mc.kernels	Mixture component kernel matrices used for the simulated data.
kernel.ellipses	N.obs x 2 x 2 array, containing the kernel matrices corresponding to each of the simulated values.
Cov.mat	True covariance matrix (N.obs x N.obs) corresponding to the simulated data.
sim.data	Simulated data values.
lambda.w	Tuning parameter for the weight function.

Examples

## Not run: 
NSconvo_sim( grid = TRUE, y.min = 0, y.max = 5, x.min = 0,
x.max = 5, N.obs = 20^2, sim.locations = NULL, mc.kernels.obj = NULL,
mc.kernels = NULL, mc.locations = NULL, lambda.w = NULL,
tausq = 0.1, sigmasq = 1, beta.coefs = 4, kappa = NULL,
covariates = rep(1,N.obs), cov.model = "exponential" )

## End(Not run)

markdrisser/convoSPAT documentation built on May 21, 2019, 11:48 a.m.