On 1/29/06, Søren Højsgaard <[EMAIL PROTECTED]> wrote:
> 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??
Possible - yes. (See the quote in the fortunes package about "This is
R. There is no if - only how.")
Roughly what one does is
- Take a copy of the fitted model object as, say, "mer"
- .Call("mer_update_y", mer, ynew, PACKAGE = "Matrix")
- arrange for a suitable call to
LMEoptimize(mer) <- controloptions
The last part is a little tricky in that "LMEoptimize<-" is hidden in
the Matrix namespace.
I'm happy to write a function to do this but my creativity is at a low
ebb and I would appreciate any suggestions for a suitable name and
calling sequence. Even more welcome would be an existing generic
function for which something like this could be a method.
> Best
> Søren
>
> ________________________________
>
> Fra: [EMAIL PROTECTED] på vegne af Peter Dalgaard
> Sendt: lø 28-01-2006 01:12
> Til: Douglas Bates
> Cc: Søren Højsgaard; [email protected]
> Emne: Re: [R] how calculation degrees freedom
>
>
>
> Douglas Bates <[EMAIL PROTECTED]> 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 (Søren 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.
>
>
> --
> O__ ---- Peter Dalgaard Øster Farimagsgade 5, Entr.B
> c/ /'_ --- Dept. of Biostatistics PO Box 2099, 1014 Cph. K
> (*) \(*) -- University of Copenhagen Denmark Ph: (+45) 35327918
> ~~~~~~~~~~ - ([EMAIL PROTECTED]) FAX: (+45) 35327907
>
>
>
>
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