DCSmooth (version 1.1.2)

sarma.sim: Simulation of a \(SARMA(p, q)\)-process

Description

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

Usage

sarma.sim(n_x, n_t, model)
qarma.sim(n_x, n_t, model)

Arguments

n_x

Number of simulated observation rows.

n_t

Number of simulated observation columns.

model

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

Value

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.

Details

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

Examples

# NOT RUN {
# 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)
summary(sarma_sim)

# }