ACEt (version 1.5.3)

AtCtEt: The ACE(t) model

Description

The ACE(t) model with the A, C and E variance components as functions with respect to age modelled by B-splines.

Usage

AtCtEt(data_m, data_d, mod = c('d','d','d'), knot_a = 5, knot_c = 5, knot_e = 5, 
boot=FALSE, num_b = 100, init = rep(0,3), robust = 0)

Arguments

data_m

An $N_m$ x 3 data matrix for MZ twins. $N_m$ is the number of MZ twin pairs. The first two columns are centered trait values (i.e. the mean should be zero) and the third column is age (or other covariates).

data_d

An $N_d$ x 3 data matrix for DZ twins. $N_d$ is the number of DZ twin pairs. The first two columns are centered trait values (i.e. the mean should be zero) and the third column is age (or other covariates).

mod

A character vector of length 3. Each element specifies the function for the A, C or E component respectively. The A and C components can be 'd'(dynamic), 'c'(constant) or 'n'(NA). The E component can only be 'd' or 'c'. Thus, $model=c('c','c','c')$ is cor

knot_a

The number of interior knots of the B-spline for the A component, which must be no less than 3. The default value is 5.

knot_c

The number of interior knots of the B-spline for the C component, which must be no less than 3. The default value is 5.

knot_e

The number of interior knots of the B-spline for the E component, which must be no less than 3. The default value is 5.

boot

A logical indicator of whether to use the bootstrap method to calculate the confidence interval. The default is FALSE.

num_b

The number of replicates when the bootstrap method is used (i.e. $boot=TRUE$). The default value is 100.

init

A 3x1 vector of the initial values for the optimization. The default values are 1.

robust

An integer indicating the number of different initial values that the function will randomly generate and try in the optimization. The default value is 0.

Value

• n_beta_a
  The number of spline coefficients for the A component.
• n_beta_c
  The number of spline coefficients for the C component.
• n_beta_e
  The number of spline coefficients for the E component.
• beta_a
  The estimated spline coefficients (if the model parameter is 'd') or variance (if the model parameter is 'c') of the A component.
• beta_c
  The estimated spline coefficients (if the model parameter is 'd') or variance (if the model parameter is 'c') of the C component.
• beta_e
  The estimated spline coefficients (if the model parameter is 'd') or variance (if the model parameter is 'c') of the E component.
• hessian
  The approximated Hessian matrix from the minus log-likelihood.
• con
  An indicator of convergence of the optimization algorithm. An integer code 0 indicates successful completion. See 'optim' for more details.
• lik
  The minus log-likelihood.
• 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.
• boot
  A list containing confidence bands estimated from the bootstrap method when $boot=TRUE$

Details

If the variance is close to the boundary (0), it is better to use the bootstrap method to get the CIs. The optimization algorithm may sometimes end up with a local minimum. It is recommended to try different random initial values by setting 'robust'.

References

He, L., Sillanp��, M. J., Silventoinen, K., Kaprio, J., & Pitk�niemi, J. (2016). Estimating Modifying Effect of Age on Genetic and Environmental Variance Components in Twin Models. Genetics, genetics-115.

Examples

data(data_ace)

result <- AtCtEt(data_ace$mz, data_ace$dz, mod=c('d','d','c'))