Options of phylogeny inference

This specifies which species is to be used to root the tree by having it become the outgroup.

This sets a threshold such that if the number of steps counted in a character is higher than the threshold, it will be taken to be the threshold value rather than the actual number of steps. The default is a threshold so high that it will never be surpassed. The use of thresholds to obtain methods intermediate between parsimony and compatibility methods is described in J. Felsenstein's 1981b paper.

In the methods which construct trees (except "Neighbor-joining and UPGMA method" and "Maximum parsimony (branch and bound search) method"), after all species have been added to the tree a rearrangements phase ensues. In most of these methods the rearrangements are automatically global, which in this case means that subtrees will be removed from the tree and put back on in all possible ways so as to have a better chance of finding a better tree. Since this can be time consuming (it roughly triples the time taken for a run) it is left as an option in some of the methods, specifically "Fitch-Margoliash and least squares method" and "Maximum likelihood method". In these methods the option "Global rearrangements" toggles between the default of local rearrangement and global rearrangement.

This allows users setting the number of replicate data sets. This defaults to 100. Most statisticians would be happiest with 1000 to 10,000 replicates in a bootstrap, but 100 gives a good rough picture.

Notice: at present POWER only allows up to 1000.

The bootstrap: bootstrapping was invented by Bradley Efron in 1979, and its use in phylogeny estimation was introduced by J. Felsenstein (Felsenstein, 1985b). It involves creating a new data set by sampling N characters randomly with replacement, so that the resulting data set has the same size as the original, but some characters have been left out and others are duplicated. The random variation of the results from analyzing these bootstrapped data sets can be shown statistically to be typical of the variation that you would get from collecting new data sets. The method assumes that the characters evolve independently, an assumption that may not be realistic for many kinds of data.

Delete-half-jackknifing: this alternative to the bootstrap involves sampling a random half of the characters, and including them in the data but dropping the others. The resulting data sets are half the size of the original, and no characters are duplicated. The random variation from doing this should be very similar to that obtained from the bootstrap. The method is advocated by Wu (1986).

Permuting species within characters: this method of resampling (well, OK, it may not be best to call it resampling) was introduced by Archie (1989) and Faith (1990; see also Faith and Cranston, 1991). It involves permuting the columns of the data matrix separately. This produces data matrices that have the same number and kinds of characters but no taxonomic structure. It is used for different purposes than the bootstrap, as it tests not the variation around an estimated tree but the hypothesis that there is no taxonomic structure in the data: if a statistic such as number of steps is significantly smaller in the actual data than it is in replicates that are permuted, then we can argue that there is some taxonomic structure in the data (though perhaps it might be just a pair of sibling species).

The option alters a step in "Maximum parsimony (branch and bound search)" which reconsiders the order in which species are added to the tree. Normally the decision as to what species to add to the tree next is made as the first tree is being constructed; that ordering of species is not altered subsequently. This option causes it to be continually reconsidered. This will probably result in a substantial increase in run time, but on some data sets of intermediate messiness it may help.

This option allows users set the expected ratio of transitions to transversions. Note that this is not the ratio of the first to the second kinds of events, but the resulting expected ratio of transitions to transversions. The exact relationship between these two quantities depends on the frequencies in the base pools.

This option is active when the substitution model "Maximum Likelihood " is selected, which distance requires that the program be provided with the equilibrium frequencies of the four bases A, C, G, and T (or U). Its default setting is one which may save users much time. If you want to use the empirical frequencies of the bases, observed in the input sequences, as the base frequencies, you simply use the default setting of this option. These empirical frequencies are not really the maximum likelihood estimates of the base frequencies, but they will often be close to those values (what they are is maximum likelihood estimates under a "star" or "explosion" phylogeny).

If the substitution model "Nei/Jin distance" is selected this option is active. This is different from the parameters used by Nei and Jin but related to them: their parameters a are related to the coefficient of variation by

The methods "Fitch-Margoliash and least squares method" and "Fitch-Margoliash and least squares method with molecular clock" carry out the method of Fitch and Margoliash (1967) for fitting trees to distance matrices. They also are able to carry out the least squares method of Cavalli-Sforza and Edwards (1967), plus a variety of other methods of the same family (see the discussion of the "POWER" option below).

The objective of these methods is to find that tree which minimizes

This option indicates that negative segment lengths are to be allowed in the tree (default is to require that all branch lengths be nonnegative).

Entry how many categories there will be (for the moment there is an upper limit of 9, which should not be restrictive).

Entry the rates for each category. These rates are only meaningful relative to each other, so that rates 1.0, 2.0, and 2.4 have the exact same effect as rates 2.0, 4.0, and 4.8. Note that a category can have rate of change 0, so that this allows us to take into account that there may be a category of sites that are invariant. (Note that the run time of the program will be proportional to the number of rate categories: twice as many categories means twice as long a run.)

This option allows the user to select among various nuclear and mitochondrial genetic codes.

As is decribed in the selection "Categories model" of option "Substitution model".