In article <4sjta2$poo at nntp3.u.washington.edu>,
Spencer Muse <muse at kurtz.bio.psu.edu> wrote:
>In article <4sgfu9$mcv at nntp3.u.washington.edu>,
>joe at evolution.genetics.washington.edu (Joe Felsenstein) writes:
>|> Actually likelihood ratio tests can also be done with three species,
>|> it is just that one of the key programs (DNAML) happens not to be
>|> able to cope with three species, for purely silly reasons. We're
>|> working on fixing that
>|> in the next major version.
>>[useful citations to many statistical relative rate tests done by a many
>>|> No one has ever
>|> specified what one does with the RRT with more than three species,
>|> nor even how to do the three species case statistically.
>>I'm not sure what you mean here, Joe. Clearly valid statistical versions
>of the relative rate test exist. Could you clarify?
I stand corrected by your useful citations. Clearly there *are* lots of
statistically formulated relative rate tests for three taxa. I note that
often people who tell me they're doing "the relative rate test" are not
doing anything statistical, nor were the original formulations of the RRT.
>|> If you want to know which branch or clade in the tree is responsible
>|> for the rate heterogeneity, one could fit trees that were clocklike
>|> except for having that branch (or all of that clade, alternatively)
>|> evolving at R times the rate of the rest of the tree. This is not
>|> easy to do with present-day programs, alas. Aside from making that
>|> possible, there are very interesting questions about how to test
>|> which branch (clade) it was that was going R times as fast.
>|>>|> But however that is to be done in a likelihood framework, the RRT has
>|> more problems as it cannot tell you how to combine all the
>|> three-taxon tests.
>>Agreed. BUT, that does not imply that doing many or all pairwise
>comparisons is a useless thing to do. And, in fact, many of the tests
>_are_ independent (this can be argued along the lines from Felsenstein's
>1985 (?) article on independent contrasts). Patterns arising from
>exhaustive pairwise tests are often amazingly strong; so strong that it
>would take incredibly high levels of correlation to explain them.
But when they *aren't* that strong you are at a loss what to do.
>will reiterate Joe's comment that it isn't at all clear how the
>collection of results should be properly interpreted. Rejection of a
>global clock test should certainly be a prerequisite for looking further
>at individual tests, reminiscent of so-called "F-protected" pairwise
>comparisons in traditional ANOVA, where differences among particular
>treatments aren't checked for unless a global F test indicates real
Certainly we need these multiple-tests procedures, analogous to
contrasts among effects in analysis of variance.
>On a cynical final note, I do find it a bit intriguing that so much
>concern is given to multiple correlated tests in one paper, but the
>problem is almost completely ignored when a result from one paper is
>followed up (in a correlated way) in other papers. (This is meant to be a
>general comment, not poined at the rr test problem in particular.)
Here I am less clear where the correlations come from. If you look at a
different locus in the second paper, how is that result correlated with
the previous one?
Joe Felsenstein joe at genetics.washington.edu (IP No. 18.104.22.168)
Dept. of Genetics, Univ. of Washington, Box 357360, Seattle, WA 98195-7360 USA