[R] how calculation degrees freedom

[Søren Højsgaard]

In connection with calculating Monte Carlo p-values based on sampled data sets: The calculations involve something like
   update(lmer.model, data=newdata)
where newdata is a simulated dataset comming from simulate(lmer.model). I guess the update could be faster if one could supply the update function with the parameter estimates from the original fit of the lmer.model as starting values. Is this possible to achieve??
Best
Søren

________________________________

Fra: pd at pubhealth.ku.dk på vegne af Peter Dalgaard
Sendt: lø 28-01-2006 01:12
Til: Douglas Bates
Cc: Søren Højsgaard; R-help at stat.math.ethz.ch
Emne: Re: [R] how calculation degrees freedom



Douglas Bates <dmbates at gmail.com> writes:


> > Of course, Monte Carlo p-values have their problems, but the world
> > is not perfect....
>
> Another approach is to use mcmcsamp to derive a sample from the
> posterior distribution of the parameters using Markov Chain Monte
> Carlo sampling.  If you are interested in intervals rather than
> p-values the HPDinterval function from the coda package can create
> those.
>

We (Søren and I) actually had a look at that, and it seems not to
solve the problem. Rather, mcmcsamp tends to reproduce the Wald style
inference (infinite DF) if you use a suitably vague prior.

It's a bit hard to understand clearly, but I think the crux is that
any Bayes inference only depends on data through the likelihood
function. The distribution of the likelihood never enters (the
hardcore Bayesian of course won't care). However, the nature of DF
corrections is that the LRT does not have its asymptotic distribution,
and mcmc has no way of picking that up.


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