extractor function for coefficient table fromsummary.mer ?
On Wed, Jun 17, 2009 at 4:59 PM, Ben Bolker<bolker at ufl.edu> wrote:
David Duffy wrote:
On Wed, 17 Jun 2009, Douglas Bates wrote:
On Tue, Jun 2, 2009 at 10:05 PM, David Duffy<David.Duffy at qimr.edu.au> wrote:
On Tue, 2 Jun 2009, Ben Bolker wrote:
?Request for comment: would it be reasonable to have the "coef" method for "summary.mer" objects return the table of parameter values, standard errors etc.?
Yes please, oh and a profile likelihood based confint.lmer() too, thanks ;).
I have been thinking about this recently and I have a way of constructing a profile likelihood for the variance component parameters. ?Are those the parameters that are of interest or are you more interested in the fixed-effects parameters?
Yes, the variance components are of direct interest.
Yet somehow the variability in estimates of variance components in much more complicated models can be expressed by quoting a standard error.
Yes, we usually try and produce appropriate confidence intervals and/or interpretable likelihood based test statistics. ?The latter, of course, are tricky mixtures for multivariate hypotheses -- a typical one for us is a variance components linkage analysis test that the common component due a particular genome region is zero for three measures (repeated at 3 occasions, but with differing contributions by occasion). ?People still want a P-value, so they can carry out adjustment for genome-wide testing (linkage is supposed to be roughly equivalent to 50-60 tests for a human length genome, but the genome-wide corrected 5% P-value is usually quoted as 2e-5). Cheers, David Duffy.
?Fixed effect profiles are interesting too (to me) ... I have written some of my own code to do this (happy to make it available), but it's not very general/robust at the moment.
I think there is a general way of creating the profiles including with respect to the fixed-effects parameters but, as always, the devil is in the details. I have already tripped up on the simplest case of models like lmer(Yield ~ 1 + (1|Batch), Dyestuff) When you condition on the value of the one and only fixed-effects parameter the code for the penalized least squares solution becomes confused because the reduced model matrix for the fixed effects has zero columns.