sim_trait_mvn: Simulate traits from a kinship matrix under the infinitesimal...

In OchoaLab/simtrait: Simulate Complex Traits from Genotypes

Simulate traits from a kinship matrix under the infinitesimal model

Description

Simulate matrix of trait replicates given a kinship matrix and model parameters (the desired heritability, group effects, total variance scale, and mean). Although these traits have the covariance structure of genetic traits, and have heritabilities that can be estimated, they do not have causal loci (an association test against any locus should fail). Below n is the number of individuals.

Usage

sim_trait_mvn(
  rep,
  kinship,
  herit,
  mu = 0,
  sigma_sq = 1,
  labs = NULL,
  labs_sigma_sq = NULL,
  tol = 1e-06
)

Arguments

rep	The number of replicate traits to simulate. Simulating all you need at once is more efficient than simulating each separately (the kinship matrix is eigendecomposed once per run, shared across replicates).
kinship	The n-by-n kinship matrix of the individuals to simulate from.
herit	The desired heritability (proportion of trait variance due to genetics).
mu	The desired parametric mean value of the trait (scalar, default 0).
sigma_sq	The desired parametric variance factor of the trait (scalar, default 1). Corresponds to the variance of an outbred individual.
labs	Optional labels assigning individuals to groups, to simulate environment group effects. Values can be numeric or strings, simply assigning the same values to individuals in the same group. If vector (single environment), length must be number of individuals. If matrix (multiple environments), individuals must be along rows, and environments along columns. The environments are not required to be nested. If this is non-NULL, then labs_sigma_sq must also be given!
labs_sigma_sq	Optional vector of environment effect variance proportions, one value for each environment given in labs (a scalar if labs is a vector, otherwise its length should be the number of columns of labs). Ignored unless labs is also given. As these are variance proportions, each value must be non-negative and sum(labs_sigma_sq) + herit <= 1 is required so residual variance is non-negative.
tol	Tolerance factor for an internal test of positive semi-definiteness of the trait covariance matrix. Procedure fails if any eigenvalues are smaller than -tol times the absolute value of the largest eigenvalue. Increase this value only if you are getting errors but you're sure your covariance matrix (the output of cov_trait()) is positive semi-definite.

Value

A rep-by-n matrix containing the simulated traits along the rows, individuals along the columns.

See Also

cov_trait(), sim_trait()

Examples

# create a dummy kinship matrix
# make sure it is positive definite!
kinship <- matrix(
    data = c(
        0.6, 0.1, 0.0,
        0.1, 0.5, 0.0,
        0.0, 0.0, 0.5
    ),
    nrow = 3
)
# draw simulated traits (matrix)
traits <- sim_trait_mvn( rep = 10, kinship = kinship, herit = 0.8 )
traits

OchoaLab/simtrait documentation built on April 19, 2024, 7:36 p.m.