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

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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

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# 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)