acetp_mcmc {ACEt} R Documentation

## 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

```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.

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

```
# 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)

```

[Package ACEt version 1.8.1 Index]