# Compute CIs for the ACE(t)-p model

### Description

Compute the posterior mean and CIs for the ACE(t)-p model using the MCMC methods

### Usage

1 | ```
acetp_mcmc(acetp, iter_num = 10000, sd = 0.1, burnin = 1000)
``` |

### Arguments

`acetp` |
An object from the 'AtCtEtp' function. |

`iter_num` |
The number of the iterations in the MCMC procedure. |

`sd` |
The standard error of the normal proposal distribution in the MCMC algorithm. The default value is 0.1. |

`burnin` |
The number of burn-in, which must be smaller than the number of iteration. |

### Value

`beta_a_mc ` |
The estimates of the spline coefficients for the A component based on the posterior mean from the MCMC method. |

`beta_c_mc ` |
The estimates of the spline coefficients for the C component based on the posterior mean from the MCMC method. |

`beta_e_mc ` |
The estimates of the spline coefficients for the E component based on the posterior mean from the MCMC method. |

`cov_mc ` |
The posterior covariance matrix of the estimates of the spline coefficients. |

`knots_a ` |
A vector of the knot positions for the A component. |

`knots_c ` |
A vector of the knot positions for the C component. |

`knots_e ` |
A vector of the knot positions for the E component. |

### Author(s)

Liang He

### References

He, L., Sillanpää, M.J., Silventoinen, K., Kaprio, J. and Pitkäniemi, J., 2016. Estimating Modifying Effect of Age on Genetic and Environmental Variance Components in Twin Models. Genetics, 202(4), pp.1313-1328.

### Examples

1 2 3 4 | ```
# data(data_ace)
# result <- AtCtEp(data_ace$mz, data_ace$dz, knot_a = 7, knot_c = 7)
# result_mc <- acetp_mcmc(result, iter_num=10000, burnin = 500)
``` |