[R-sig-ME] Approximation to Marginal Likelihood of GLMM in lme4‏

[Ken Beath]

It is in the package documentation, either Laplace or adaptive
Gauss-Hermite is available.

For optimization methods use a quasi-Newton (BFGS) if it works, Nelder-Mead
otherwise, I would avoid the others.

These are questions you should have been able to answer yourself.


On 25 November 2014 at 02:24, Lee Yan Liang <yanlianglee at live.com> wrote:

> Hi Ben,
>
> I suppose lme4 assumes the random effects has a multivariate normal
> distribution with mean vector 0 and
> a variance-covariance matrix. In that case, the marginal likelihood
> (obtained by integrating out the likelihood
> and random effect distributions) cannot be obtained in closed-form.
>
> I am not sure what type of approximations (quadrature, laplace, PQL etc.)
> do you use in lme4 for generalised
> linear mixed effects model? And do you have any good advice in choosing
> optimisers available in the package
> optimx?
>
> Thank you.
>
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>
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*Ken Beath*
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