Farris James S, Albert Victor A, Källersjö Mari, Lipscomb Diana, Kluge Arnold G
Molekylärsystematiska laboratoriet, Naturhistoriska riksmuseet, Box 50007, Stockholm, S 104 05, Sweden.
New York Botanical Garden, Bronx, New York, 10458-5126, U.S.A.
Cladistics. 1996 Jun;12(2):99-124. doi: 10.1111/j.1096-0031.1996.tb00196.x.
Abstract- Because they are designed to produced just one tree, neighbor-joining programs can obscure ambiguities in data. Ambiguities can be uncovered by resampling, but existing neighbor-joining programs may give misleading bootstrap frequencies because they do not suppress zero-length branches and/or are sensitive to the order of terminals in the data. A new procedure, parsimony jackknifing, overcomes these problems while running hundreds of times faster than existing programs for neighbor-joining bootstrapping. For analysis of large matrices, parsimony jackknifing is hundreds of thousands of times faster than extensive branch-swapping, yet is better able to screen out poorly-supported groups.
摘要——由于邻接法程序旨在生成唯一的一棵树，所以它们可能会掩盖数据中的模糊性。可以通过重采样来发现模糊性，但现有的邻接法程序可能会给出误导性的自展频率，因为它们不会抑制零长度分支和/或对数据中端粒的顺序敏感。一种新的方法，即简约法刀切法，克服了这些问题，同时运行速度比现有的邻接自展程序快数百倍。对于大型矩阵的分析，简约法刀切法比广泛的分支交换快数十万倍，而且更能筛选出支持度低的类群。
