sarma.sim {DCSmooth}R Documentation

Simulation of a SARMA(p, q)-process


sarma.sim simulates a specified SARMA-model on a lattice with normally distributed innovations.


sarma.sim(n_x, n_t, model)

qarma.sim(n_x, n_t, model)



Number of simulated observation rows.


Number of simulated observation columns.


A list containing the coefficient matrices ar and ma of the SARMA model as well as the standard deviation of innovations sigma.


The function returns an object of class "sarma", consisting of

Y A n_x \times n_t-matrix of simulated values of the specified SARMA process.
innov The innovations used for simulation, iid. drawn from a normal distribution with zero mean and variance \sigma^2.
model The model used for simulation, inherited from input.
stnry An logical variable indicating whether the simulated model is stationary.


Simulation of a top-left dependent spatial ARMA process (SARMA). This function returns an object of class "sarma". The simulated innovations are created from a normal distribution with specified variance \sigma^2.

see the vignette for further details.

See Also

sarma.est, sfarima.est


# See vignette("DCSmooth") for examples and explanation
ma <- matrix(c(1, 0.2, 0.4, 0.1), nrow = 2, ncol = 2)
ar <- matrix(c(1, 0.5, -0.1, 0.1), nrow = 2, ncol = 2)
sigma <- 0.5
sarma_model <- list(ar = ar, ma = ma, sigma = sigma)

sarma_sim <- sarma.sim(100, 100, model = sarma_model)

[Package DCSmooth version 1.1.2 Index]