Re: About the Bootstrap...

> But is it anything like this?: Marshall, C. R. 1991. Statistical
> tests and bootstrapping: assessing the reliability of phylogenies based
> on distance data. Mol. Biol. Evol. 8:386-391. Or this: Krajewski, C.,
> and A. W. Dickerman. 1990. Bootstrap analysis of phylogenetic trees
> derived from DNA hybridization distances. Syst. Zool. 39:383-390. One of
> them (I forget which) fills pseudoreplicate cells by choosing from a
> normal distribution with mean at the real value and standard deviation
> determined by experimental measurements. (The other picks at random from
> among experimental measurements for that cell.)

Well, I didn't know these two references -- Thanks! I'll look at them, but I am
afraid that's not exactly my topic.


> If you want to use between-character covariances you will have to write
> your own program. Otherwise there are a number of programs that can
> evolve characters on a tree using brownian motion models. But all of
> them assume independence among traits.

Comment on the last sentence. -- Yes, and this "generalized" assumption is
something very strange for me. Anyone who studies morphological traits knows that
correlations between characters (e.g., as by-products of developmental and
morpho-functional constraints) are the rule, and independance is actually unlikely
to be observed... But everybody estimates trees using parsimony or phenetics
without taking into account this elementary fact in the computation!!! This is
particularly true in cladistic analysis: to be able to identify 100, if not more,
"independent" characters on highly integrated structures such as mammalian teeth
always makes me very sceptic about the reliability of the inferred phylogenies --
and I am a paleo-mammalogist working on morphological data, not a molecular
phylogenetician!


> > The procedure proposed in (ii) is much
> > more easy and straightforward, but I currently don't know any paper dealing
> > with it on a theoretical basis. For instance, when I apply this procedure on
> > real data sets (I have done the programs for this), and I compare estimated
> > CL with CL estimates obtained by classical nonparametric bootstrap, it (the
> > ii procedure) appears to return significantly higher CL estimates, and thus
> > to be significantly less conservative than the nonparametric bootstrap
> > technique. But how to interpret such differences?
>
> The standard method is to use simulations to assess the behavior of data
> sets when the true tree is known. That's a lot of work, but I don't see
> another way.

I don't see too!... And that's exactly why I try to know if this work has not
already be done... It seems to me that the "parametric" procedure should logically
be less conservative and more powerfull than the nonparametric procedure, but
"logic" is not always our best friend...

Gilles.