Simulate a Spatial MA Process

Simulates white noise with linear dependencies (\(K\)) $$X_i^{(1)} = \sum_{j}\epsilon_{i-j}^{(1)}K_{j}$$

Usage

gridMA(N, M, K, distribution = "normal")

Arguments

N

Rows

M

Columns of grid

K

Numeric matrix of kernel weights

distribution

Type of distribution must be one of "normal", "uniform", "cauchy", "chisq"

Value

Numeric matrix with N rows and M columns

Examples

set.seed(1)
K <- MA_coef_all(0.3)
x <- gridMA(25, 25, K)
image(x)