| 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. |
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
# 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)