I have 4 questions regarding ML analysis. Thanks for any insights.
1/ I am analyzing nuclear data sequences for which I have some sites coded as ambiguities (following a IUB code). I am wondering how the last Paup version exactly treats such positions during a ML search. Although heterozygotie may not be informative at the taxonomic level I'm working, I still want to include these positions because some of them are heterozygous in only one sequence and parsominy informative in others.
2/The last updated version of Paup provides a table including branch lengths, their standard errors and a LRT test under the null hypothesis that a branch has zero length. For some branches I have a standard error that is weakly higher or equal to the branch length itself. However, the LRT tests still tells me that these branches are significantly different from zero and thus statistically do exist. I don't get that.
3/ I did a ML run based on a "branch and bound" search and it took 6 hours 45 mn, leading to a unique best tree. I then did an exhaustive search with the same settings and it took 45 mn (I have 7 taxa), leading to a different tree with a slightly higher -Ln L value. Some people told me here that "branch and bound" is good for parsimony criteria but may get lost in ML search because it is dealing with probabilities and not number of steps. Is that so true that a branch and bound method is not highly commendable for ML search??
4/ Finally what is exactly the -ln L value unconstrained? Is it a value calculated based on a star tree or on data patterns without topological reference. Then, if one has sequence data for the same gene in two different groups of taxa, does it mean something to use the -ln L unconstrained value or a ratio between that value and the best tree value to perform between groups comparison (having or not the same number of taxa, the same number of branches and the same "type" of topology). Or is it meaningless or exactly as informative than just comparing the % of informative sites between these groups.
Thank you very much and have a nice day.
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