ar1_sim_conditional: Simulate from a stationary Gaussian AR(1) process.
In irregulAR1: Functions for Irregularly Sampled AR(1) Processes

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.

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)