sarma.sim {DCSmooth} | R Documentation |
SARMA(p, q)
-processsarma.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)
n_x |
Number of simulated observation rows. |
n_t |
Number of simulated observation columns. |
model |
A list containing the coefficient matrices |
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 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)