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).
knot_a
The number of interior knots of the B-spline for the A component. The default value is 8.
knot_c
The number of interior knots of the B-spline for the C component. The default value is 8.
knot_e
The number of interior knots of the B-spline for the E component. The default value is 8.
eps
Tolerance for convergence of the EM algorithm iterations. The default value is 0.1.
mod
A character vector of length 3. Each element specifies the function for the A, C or E component respectively. The components can be 'd'(dynamic), 'c'(constant) or 'l'(linear). The default is c('d','d','d').
Value
var_b_aThe estimated variance for the penalized coefficient for the A components.
var_b_cThe estimated variance for the penalized coefficient for the C components.
var_b_eThe estimated variance for the penalized coefficient for the E components.
beta_aThe estimated spline coefficients of the A component. See 'details' for more information.
beta_cThe estimated spline coefficients of the C component. See 'details' for more information.
beta_eThe estimated spline coefficients of the E component. See 'details' for more information.
conAn indicator of convergence of the optimization algorithm. An integer code 0 indicates successful completion. See 'optim' for more details.
likThe minus log marginal likelihood.
knot_aA vector of the knot positions for the A component.
knot_cA vector of the knot positions for the C component.
knot_eA vector of the knot positions for the E component.
Details
When the 'mod' parameter for a component is 'd'(dynamic), the corresponding 'beta' is the spline coefficients.
When the 'mod' parameter for a component is 'l'(linear), the corresponding 'beta' is a vector of two values, the exponential of which (exp(beta)) are the variances at the minimum and maximum age (or other covariates) provided in the data.
When the 'mod' parameter for a component is 'c'(constant), the corresponding 'beta' has only one value and exp(beta) is the variance.
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.