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

## Description

Simulate from a stationary Gaussian AR(1) process at `n` consecutive time points.

## Usage

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

## Arguments

 `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.

## Value

A vector of length `length(pred_times)` with the process values.

## Examples

 ``` 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) ```

irregulAR1 documentation built on May 2, 2019, 8:49 a.m.