Again: The initial model is invalid because state has a zero probability.

[seta...@gmail.com]

Dear all,

I hope that you can help me. I'm new to beast and keep getting the following error:

The individual components of the posterior are as follows:

The initial posterior is zero: 

  CompoundLikelihood(compoundModel)=(

    Uniform(tmrca(untitled1))=-Inf, 

    Gamma(ac)=-3.184, 

    Gamma(ag)=-3.1687, 

    Gamma(at)=-3.184, 

    Gamma(cg)=-3.184, 

    Gamma(gt)=-3.184, 

    Uniform(frequencies)=0.0, 

    Exponential(alpha)=-0.3069, 

    Exponential(ucld.stdev)=0.0986, 

    CTMCScalePrior(ctmcScalePrior)=-1.5581, 

    Uniform(yule.birthRate)=-230.2585, 

    BirthDeathGernhard08Model/yule(treeModel)=-1115.2126

    Total = -Infinity

  ), 

  CompoundLikelihood(compoundModel)=(

    BeagleTreeLikelihood(treeLikelihood)=-222515.5008, 

    DiscretizedBranchRates(discretizedBranchRates[branchRates])=-799.89

    Total = -223315.39086164942

  )

  Total = -Infinity

For more information go to <http://beast.bio.ed.ac.uk/>.

java.lang.RuntimeException: Terminate

at dr.app.beast.BeastMain.<init>(Unknown Source)

at dr.app.beast.BeastMain.main(Unknown Source)

I do have a starting tree (a RaxML best tree) and I have not assigned any monophyllies to my data set (as suggested by other posts that if the assigned monophylly and the one in the tree aren't the same you will get this error) . I really don't know what is going wrong here. I rather not assign a monophylly as I want beast to go through my data independently. I really hope that you can help me.

My best

Setareh

[Andrew Rambaut]

Dear Setareh,

This error is because the TMRCA of the taxon set called ‘untitled1’ in the starting tree lies outside the boundaries of the uniform prior you have placed on this TMRCA. The solution is to set the ‘age’ of the TMRCA to lie in the middle of the range (the age is set in the Taxon Set table). I would also recommend setting it to enforce monophyly. 

Generally I would avoid using a specified starting tree - it is generally preferable to use a random starting tree and run the chain twice to ensure you get the same results (you can combine these runs afterwards). 

Andrew

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