sarma.sim {DCSmooth} | R Documentation |

## 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 |

### 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

### 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)
sigma <- 0.5
sarma_model <- list(ar = ar, ma = ma, sigma = sigma)
sarma_sim <- sarma.sim(100, 100, model = sarma_model)
summary(sarma_sim)
```

[Package

*DCSmooth*version 1.1.2 Index]