[R] Fwd: How to sample from a linear mixed model

[Douglas Bates]

On 6/20/05, Søren Højsgaard <Soren.Hojsgaard at agrsci.dk> wrote:
> Thanks. I wonder if there is a general way of extracting var(u) and var(e), which would be needed to simulate u and e. Clearly, one can get the estimated parameters, but is there a clever way of 'setting up' the matrices??
> Best
> Søren

The short answer is that the result of VarCorr applied to a fitted
lmer model gives the variance-covariance matrix of the random effects
and the variance of the noise term.  I enclose a function that can be
used to simulate data somewhat more efficiently from a fitted lmer
model.  It uses some of the intermediate values that have been stored
in the object.

At least I think this lmerSim function works.  It takes two arguments,
the first is the fitted model and the second is the number of data
sets to simulate.  It uses the estimated fixed effects from the model.
 An enhancement would be to allow the fixed effects to be given as a
third argument.

 
> On 6/19/05, Søren Højsgaard <Soren.Hojsgaard at agrsci.dk> wrote:
> > I would like to draw a sample from a linear mixed model y=Xb+Zu+e which has been fitted with lme(), i.e. a model y ~ N(Xb, C), where C=Z cov(u) Z' + cov(e).
> > I've tried to figure out how to extract C from an lme object, because that would solve my problem when also using the predict() function, but without any luck.
> > Can anyone help on that?
> 
> C is not stored in an lme object.  In fact it is never created.
> (Consider the dimensions of this matrix.  It could be huge.)
> 
> The easiest way to simulate data from a linear mixed model is to
> simulate u and e then form Xb+Zu+e
> 
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