sfarima.sim {DCSmooth}

Simulation of a SFARIMA(p, q, d)-process

Description

sfarima.sim simulates a specified SFARIMA-model on a lattice with normally distributed innovations.

Usage

sfarima.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 QARMA model, the long memory parameter vector d as well as the standard deviation of innovations sigma.

Value

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

Y	A n_x \times n_t-matrix of simulated values of the specified SFARIMA 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 separable spatial fractionally ARIMA process (SFARIMA). This function returns an object of class "sfarima". The simulated innovations are created from a normal distribution with specified variance \sigma^2.

see the vignette for further details.

See Also

qarma.est

Examples

# 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)
d <- c(0.1, 0.1)
sigma <- 0.5
sfarima_model <- list(ar = ar, ma = ma, d = d, sigma = sigma)

sfarima_sim <- sfarima.sim(100, 100, model = sfarima_model)
surface.dcs(sfarima_sim$Y)