[R-sig-ME] Teaching Mixed Effects
bolker at ufl.edu
Fri Jan 30 19:55:14 CET 2009
Raubertas, Richard wrote:
[much more snippage]
> Some will probably say we should focus on (interval) estimation of
> rather than testing. But this doesn't solve the problem at hand.
> how this discussion started: the difficulty of calculating reliable
> for fixed effects. Because of the duality between hypothesis tests and
> intervals, if you can't get reliable p-values, then essentially by
> you can't get reliable confidence intervals either. So the issue of
> versus-estimation is a red herring with respect to the deeper problem of
> inference for fixed effects in mixed models.
> Others may try to dodge the problem by claiming to be Bayesians,
> credible intervals from posterior distributions. But this won't hold
> if they are using lme4 and mcmcsamp. No honest Bayesian can use an
> "uninformative" prior (if such a thing even exists), or at least not
> than once in any area of research: after analysis of one data set, the
> posterior from that analysis should inform the prior for the next, but
> has its priors hard-coded. I think the real rationale for mcmcsamp is
> hope that it will produce results with good frequentist properties. I
> not aware that this has been demonstrated for mixed models in the
> statistics literature.
If mcmcsamp worked at the moment I would strongly suggest that we
should be trying to figure out how its conclusions differ from those
based on other priors (e.g. Gelman's half-Cauchy priors for variances).
>> It is unhelpful of me not to provide p-values in the lmer summaries
>> but I develop the software out of interest in doing it as well as I
>> possibly can and not because someone assigns me a task to compute
>> something. I really don't know of a good way of assigning p-values to
>> t-ratios or F-ratios so I don't. I still report the ratio of the
>> estimate divided by it standard error, and even call it a t-ratio,
>> because I think it is informative.
> Doug, can you elaborate on that last clause? In what way is the
> ratio |T| informative that the monotonic transformation 2*(1 -
> is not? In other words, if a p-value (based in this case on a standard
> normal) is not reliable for inference, what inferential value does T
> Less formally, if T = 2, for example, what exactly do you conclude about
> the parameter, and what is your confidence in that conclusion?
Perhaps it's just that people are a little less likely to
take it too seriously?
> [ ... ]
>> I feel that the likelihood ratio is a perfectly reasonable way of
>> comparing two model fits where one is a special case of the other. In
>> fact, if the models have been fit by maximum likelihood, the
>> likelihood ratio would, I think, be the first candidate for a test
>> statistic. The problem with likelihood ratio tests is not the
>> likelihood ratio, per se -- it is converting the likelihood ratio to a
>> p-value. You need to be able to evaluate the distribution of the
>> likelihood ratio under the null hypothesis. The chi-square
>> approximation to the distribution is exactly that - an approximation -
>> and its validity depends on not testing at the boundary and on having
>> a large sample, in some sense of the sample size. If I were really
>> interested in evaluating a p-value for the likelihood ratio I would
>> probably try a parametric bootstrap to get a reference distribution.
> Yes, but the problem is that (as you noted earlier in your post) the
> distribution depends on the values of the nuisance parameters, except in
> very special cases. A simple parametric bootstrap conditions on the
> estimated values of those parameters, as if they were known.
> a function to do this might be a useful addition to lme4.
Well, this does already exist, more or less, in the form of
the simulate() method for mer objects: see
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