Description Usage Arguments Value Examples

Simulate from a stationary Gaussian AR(1) process at `n`

consecutive
time points.

1 2 | ```
ar1_sim_conditional(pred_times, obs_times, x_obs, rho, sigma, mu_pred = 0,
mu_obs = 0)
``` |

`pred_times` |
A vector of time points to simulate at. |

`obs_times` |
A vector of time points at which observations have been made. |

`x_obs` |
The observed values of the process. |

`rho` |
A real number strictly less than 1 in absolute value. |

`sigma` |
A positive real number. |

`mu_pred` |
A vector or scalar with expected values. |

`mu_obs` |
A vector or scalar with expected values. |

A vector of length `length(pred_times)`

with the process
values.

1 2 3 4 5 6 7 8 9 10 11 12 | ```
t_pred <- c(1, 3, 6:8, 10)
t_obs <- c(2, 5, 11:12)
x_obs <- rnorm(4)
rho <- 0.5
sigma <- 1
# Means equal 0
ar1_sim_conditional(t_pred, t_obs, x_obs, rho, sigma)
# Time-varying means
mu_pred <- t_pred + rnorm(length(t_pred))
mu_obs <- t_obs + rnorm(length(t_obs))
ar1_sim_conditional(t_pred, t_obs, x_obs + mu_obs, rho, sigma,
mu_pred, mu_obs)
``` |

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