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Not That It Get's Lost – My Comment To A (Nice) Dating Of Dravidian Languages

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branch support, languages
Monday, 12 November 2018
Twitter pointed me to a recent dating newspaper of Dravidian languages past times http://www.rachelteodoro.com/search?q=
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Some to a greater extent than technical notes:
It is unremarkably seen, but 1 should non study a PP = 1.0 for those branches that receive got been constrained to endure monophyletic; the PP is literally the probability for the branch, which is unknown, if its non gratuitous to endure modified. You tin score the constrained branch e.g. past times asterisk or write ":=1.00" instead of "1.00"
It has been lately shown for imitation binary together with real-world total
testify data, which receive got like signal issues than seen hither (Bayesian
chronograms amongst many PP << 1.0 branches), that MCC topologies
tin endure problematic (O'Reilly JE, Donoghue PCJ. 2017. The
efficacy of consensus tree methods
for summarising phylogenetic relationships from a posterior sample of
trees estimated from morphological data. Systematic Biology https://academic.oup.com/sy....
The authors recommend using MRC tree for dating. Your MRC tree likely approaches a comb. Nonetheless,
an interesting experiment would be, if the dating estimates for the
unresolved MRC would endure substantially different. My guess is, non then
much (noting the quite large but consistent HPD-intervals shown inwards Figs 3–5).

Just around tips for the future, how to bargain amongst the topological uncertainty. Utterly harmless, right? I receive got no idea, why they are reluctant to receive got this out. Maybe it's because I'm non a linguist, my comments postulate to a greater extent than scrutiny than the other 4 accepted (all inside a few hours or less) then far (two of them submitted later hours later I pressed "send"). Maybe it's the link to our network weblog (which is non 1 making whatever money).

Update (23/03 17:30)
The comment is yet non online, but cheers to this post together with Twitter (modern times, social media beets opened upwards scientific discipline give-and-take platforms), I got already a reply: the Bayesian consensus network for the information every bit tweet.
Simon tweeted that it's non real interesting, but I detect it real interesting. Especially since it's a skillful gibe amongst a ML bootstrap back upwards network using the same data, but a unproblematic binary exchange model.

We should all simply tweet the graphical results that nosotros don't (or couldn't) publish.

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