[R-sig-ME] Subject-wise log-likelihood gradient and hessian
bbolker at gmail.com
Mon Oct 1 00:47:02 CEST 2012
Rubem Kaipper Ceratti <rubem_ceratti at ...> writes:
> Hi all,
> I'd like to know if it's possible to extract the subject-wise
> gradient and hessian of the marginal
> log-likelihood from a 'mer' object.
Can you explain in slightly greater detail? What are the
parameters you're taking the gradient and hessian with respect
to -- sounds like the fixed-effect parameters (beta)? How do
you define "subject-wise"? The marginal likelihood is only
defined with respect to the full data set, isn't it?
> Using the 'cbpp' dataset as an example:
> (m1 <- glmer(size ~ period+(1|herd), cbpp, poisson))
> Since apparently there's no way to get what I need directly, I've
> thought about splitting the data frame by 'herd', updating m1 on
> each subset and then calling numDeriv::grad() and
> numDeriv::hessian() on the deviance function of each updated object
> at the estimates from m1, but I couldn't get this approach to work.
> Any input is appreciated.
I assume you're already using the development version, as
the deviance function is only available in the development version?
> Thanks (and sorry for the broken english),
English seems fine -- it's the technical description that's
eluding me a bit.
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