Linear mixed model data simulation
Simulate data under a linear mixed model, using the eigen decomposition of the variance matrix.
lmm.simu(tau, sigma2, K, eigenK = eigen(K), X, beta)
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 |
The data are simulated under the following linear mixed model :
Y = X beta + omega + epsilon
with omega ~ N(0, tau K) and epsilon ~ N(0, sigma^2 I_n).
The simulation uses K only through its eigen decomposition; the parameter
K is therefore optional.
A named list with two members:
y |
Simulated value of Y |
omega |
Simulated value of omega |
Hervé Perdry and Claire Dandine-Roulland
# 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)
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