lmm.simu {gaston}

Linear mixed model data simulation

Description

Simulate data under a linear mixed model, using the eigen decomposition of the variance matrix.

Usage

 lmm.simu(tau, sigma2, K, eigenK = eigen(K), X, beta) 

Arguments

tau

Model parameter

sigma2

Model parameter

K

(Optional) A positive symmetric matrix K

eigenK

Eigen decomposition of K

X

Covariable matrix

beta

Fixed effect vector of covariables

Details

The data are simulated under the following linear mixed model :

Y = X\beta + \omega + \varepsilon

with \omega \sim N(0,\tau K) and \varepsilon \sim N(0,\sigma^2 I_n) .

The simulation uses K only through its eigen decomposition; the parameter K is therefore optional.

Value

A named list with two members:

y

Simulated value of Y

omega

Simulated value of \omega

Author(s)

Hervé Perdry and Claire Dandine-Roulland

See Also

random.pm

Examples

# generate a random positive matrix 
set.seed(1)
R <- random.pm(503)

# simulate data with a "polygenic component" 
y <-  lmm.simu(0.3, 1, eigenK = R$eigen)
str(y)