kinsim: Simulate Biometrically Informed Multivariate Data

Description

Generates paired multivariate data for kinship pairs based on specified ACE (Additive genetic, Common environment, unique Environment) parameters with covariance structure.

Usage

kinsim(
  r_all = c(1, 0.5),
  c_all = 1,
  npg_all = 500,
  npergroup_all = rep(npg_all, length(r_all)),
  mu_all = 0,
  variables = 2,
  mu_list = rep(mu_all, variables),
  r_vector = NULL,
  c_vector = NULL,
  ace_all = c(1, 1, 1),
  ace_list = matrix(rep(ace_all, variables), byrow = TRUE, nrow = variables),
  cov_a = 0,
  cov_c = 0,
  cov_e = 0,
  ...
)

Value

A data frame with the following columns:

Ai_1: genetic component for variable i for kin1
Ai_2: genetic component for variable i for kin2
Ci_1: shared-environmental component for variable i for kin1
Ci_2: shared-environmental component for variable i for kin2
Ei_1: non-shared-environmental component for variable i for kin1
Ei_2: non-shared-environmental component for variable i for kin2
yi_1: generated variable i for kin1
yi_2: generated variable i for kin2
r: level of relatedness for the kin pair
id: Unique identifier for each kinship pair

Arguments

r_all: Numeric vector. Levels of genetic relatedness for each group; default is c(1, 0.5) representing MZ and DZ twins respectively.
c_all: Numeric. Default shared variance for common environment; default is 1.
npg_all: Integer. Default sample size per group; default is 500.
npergroup_all: Numeric vector. Sample sizes by group; default repeats npg_all for all groups in r_all.
mu_all: Numeric. Default mean value for all generated variables; default is 0.
variables: Integer. Number of variables to generate; default is 2. Currently limited to a maximum of two variables.
mu_list: Numeric vector. Means for each variable; default repeats mu_all for all variables.
r_vector: Numeric vector. Alternative specification providing genetic relatedness coefficients for the entire sample; default is NULL.
c_vector: Numeric vector. Alternative specification providing shared-environmental relatedness
ace_all: Numeric vector. Default variance components in order c(a, c, e) for all variables; default is c(1, 1, 1).
ace_list: Matrix. ACE variance components by variable, where each row represents a variable and columns are a, c, e components; default repeats ace_all for each variable.
cov_a: Numeric. Shared variance for additive genetics between variables; default is 0.
cov_c: Numeric. Shared variance for shared-environment between variables; default is 0.
cov_e: Numeric. Shared variance for non-shared-environment between variables; default is 0.
...: Additional arguments passed to other methods.

Details

This function extends the univariate ACE model to multivariate data, allowing simulation of correlated phenotypes across kinship pairs with different levels of genetic relatedness. It supports simulation of up to two phenotypic variables with specified genetic and environmental covariance structures.

Examples

Run this code

# Generate basic multivariate twin data with default parameters
twin_data <- kinsim()

# Generate data with genetic correlation between variables
correlated_data <- kinsim(cov_a = 0.5)

# Generate data for different relatedness groups with custom parameters
family_data <- kinsim(
  r_all = c(1, 0.5, 0.25), # MZ twins, DZ twins, and half-siblings
  npergroup_all = c(100, 100, 150), # Sample sizes per group
  ace_list = matrix(
    c(
      1.5, 0.5, 1.0, # Variable 1 ACE components
      0.8, 1.2, 1.0
    ), # Variable 2 ACE components
    nrow = 2, byrow = TRUE
  ),
  cov_a = 0.3, # Genetic covariance
  cov_c = 0.2 # Shared environment covariance
)

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