Not sure if a function for that already exists. You could plot posterior
distributions at each node (or perhaps, nodes of interest) if that is workable
graphically…Alternatively you could print the numerical 95% ci or HPD interval
at each node, along with mean or median value (for a bayesian recon).
> On Feb 27, 2017, at 11:16 AM, Arbuckle, Kevin <k.arbuc...@liverpool.ac.uk>
> Hi all,
> I was wondering about plotting the output of an ancestral state
> 'reconstruction' of a continuous trait while incorporating at least some of
> the uncertainty around the estimates.
> One approach I thought of was to map the ASR onto a tree in a standard way,
> then at each node have essentially a mini-legend that is of a length
> reflecting the width of the confidence interval of the estimate at that node,
> and is coloured on the same colour-scale as the overall tree legend. For
> instance, if the colour scheme for the tree goes from blue through yellow to
> red as the value increases, then a node with a relatively precise and high
> estimate will have a short bar only ranging through different shades of red,
> whereas a highly uncertain low estimate will have a wider bar coloured from
> (say) dark blue to orange/light red. I hope that description makes sense.
> I was wondering if anyone is aware of a function that already implements such
> an approach, otherwise I'll try to put one together myself. I am aware of
> phytool's fancyTree(type="phenogram95") as a way of incorporating uncertainty
> into a plotted ASR for continuous traits. However, this often results in
> difficulty in distinguishing different nodes where estimates are similar and
> also does not lend itself easily to, for instance, plotting pie charts
> representing discrete trait ASRs onto a tree mapped with a continuous trait.
> Hence I can imagine a more general approach as above but don't want to
> duplicate effort if a function already exists (and also if others feel this
> is a useful idea it can be added to existing packages If I share it as above).
> Best wishes,
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