I think people have replied and covered most of your questions (if not let me
know!). In terms of running fitDiscrete more than once, currently the function
tries 10 fixed starting points and 10 random ones, all in the range specified
by qLimits. This is usually (but not always!) sufficient. You might try
changing qLimits to a different (higher?) range before trying to rerun the
function.
Luke
On Oct 12, 2010, at 5:08 PM, Lara Poplarski wrote:
> Dear List,
>
> I am new to R, and hope someone can kindly help with the following
> task. I have a Bayesian sample of trees in nexus format and discrete
> data; example trees and data are at the bottom of the email. I would
> like to use fitDiscrete in geiger to estimate parameter lambda for all
> variables. The idea is to then compare the distributions across trees
> of lambda values for the different variables.
>
> I have been using the following:
>
> for(i in 1:5) {
> cat("t",i,"\n");
> fd<-fitDiscrete(trees[[i]],data,treeTransform="lambda")
> print(fd)
> }
>
> A few specific questions:
>
> 1. Since trees in my sample are non-ultrametric, I get the following warning:
> Warning: some tree transformations in GEIGER might not be sensible for
> nonultrametric trees.
>
> Is the lambda transformation sensible for non-ultrametric trees? I
> could not find further information on this in the manual or in the
> Pagel references.
>
> 2. In some cases, I get the following warning:
> [1] "Warning: may not have converged to a proper solution."
>
> Does it make sense to get fitDiscrete to repeat the ML estimation for
> these cases, until convergence? Can someone suggest how to modify the
> loop above to do this?
>
> 3. Finally, I would be grateful for pointers on how to tackle the
> output. For example, can someone suggest how to go about calculating
> the mean lambda value, across trees, for each of the 4 variables?
>
> I also have a more general question. Both the tree sample and dataset
> are relative large (1000 trees * 80 variables), so based on
> preliminary runs I expect the analysis to take rather long. Any
> suggestions on how to speed things up and/or on a different approach
> to the tree sample/multiple variable set-up would be most welcome!
>
> Many thanks in advance,
> Lara Poplarski
>
> =====
> trees
> =====
> #NEXUS
> [R-package APE, Tue Oct 12 15:40:42 2010]
>
> BEGIN TAXA;
> DIMENSIONS NTAX = 5;
> TAXLABELS
> taxon4
> taxon5
> taxon1
> taxon2
> taxon3
> ;
> END;
> BEGIN TREES;
> TRANSLATE
> 1 taxon4,
> 2 taxon5,
> 3 taxon1,
> 4 taxon2,
> 5 taxon3
> ;
> TREE * UNTITLED = [&R]
> (((1:0.03870620631,2:0.03870620631):0.01327593656,(3:0.009785519975,4:0.009785519975):0.04219662289):0.9758290676,5:1.02781121);
> TREE * UNTITLED = [&R]
> (5:1.546301171,((1:0.1587038879,(2:0.1024085511,3:0.1024085511):0.05629533677):1.183393702,4:1.34209759):0.204203581);
> TREE * UNTITLED = [&R]
> (2:0.09245329862,(3:0.07043250347,(1:0.7179110514,(4:0.8067701138,5:0.03902596165):0.9586401181):0.6771807231):0.4711165503);
> TREE * UNTITLED = [&R]
> ((4:0.3565618952,3:0.6832435813):0.6823437791,((5:0.6852910472,1:0.5428841521):0.9435752912,2:0.7694561274):0.5071240654);
> TREE * UNTITLED = [&R]
> ((4:0.2373158785,3:0.3414158912):0.9065216579,(2:0.995543058,(5:0.8844613975,1:0.7886170356):0.9908593148):0.627468311);
> END;
>
> =====
> data
> =====
>
> V01 V02 V03 V04
> taxon1 h h a <NA>
> taxon2 g h d <NA>
> taxon3 h j h h
> taxon4 g h g <NA>
> taxon5 i j <NA> a
>
> _______________________________________________
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Luke Harmon
Assistant Professor
Biological Sciences
University of Idaho
208-885-0346
[email protected]
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