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gaston (version 1.2)

lmm.simu: Linear mixed model data simulation

Description

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

Usage

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

Arguments

Value

A named list with two members:ySimulated value of $Y$omegaSimulated value of $\omega$

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.

See Also

random.pm

Examples

Run this code
# 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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