ACEt (version 1.4)

acetp_mcmc: Compute CIs for the ACE(t)-p model

Description

Compute CIs for the ACE(t)-p model using the MCMC methods and the bootstrap method

Usage

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

Arguments

acetp

An object of the AtCtEtp_model (or AtCtEp_model, AtEtp_model).

iter_num

The number of MCMC iteration.

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_a_mc
  The posterior covariance matrix of the estimates of the spline coefficients for the A component.
• cov_c_mc
  The posterior covariance matrix of the estimates of the spline coefficients for the C component.
• cov_e_mc
  The posterior covariance matrix of the estimates of the spline coefficients for the E component.

References

Liang He, Mikko J. Sillanp��, Karri Silventoinen, Jaakko Kaprio, Janne Pitk�niemi, Estimating modifying effect of age on genetic and environmental variance components in twin models. Genetics, 2016

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)