Goloboff Pablo A
CONICET, INSUE, Instituto Miguel Lillo, 4000, S.M. de Tucumán, Argentina.
Cladistics. 2014 Jun;30(3):260-272. doi: 10.1111/cla.12047. Epub 2013 Jul 19.
Several extensions to implied weighting, recently implemented in TNT, allow a better treatment of data sets combining morphological and molecular data sets, as well as those comprising large numbers of missing entries (e.g. palaeontological matrices, or combined matrices with some genes sequenced for few taxa). As there have been recent suggestions that molecular matrices may be better analysed using equal weights (rather than implied weighting), a simple way to apply implied weighting to only some characters (e.g. morphology), leaving other characters with a constant weight (e.g. molecules), is proposed. The new methods also allow weighting entire partitions according to their average homoplasy, giving each of the characters in the partition the same weight (this can be used for dynamically weighting, e.g. entire genes, or first, second, and third positions collectively). Such an approach is easily implemented in schemes like successive weighting, but in the case of implied weighting poses some particular problems. The approach has the peculiar implication that the inclusion of uninformative characters influences the results (by influencing the implied weights for the partitions). Last, the concern that characters with many missing entries may receive artificially inflated weights (because they necessarily display less homoplasy) can be solved by allowing the use of different weighting functions for different characters, in such a way that the cost of additional transformations decreases more rapidly for characters with more missing entries (thus effectively assuming that the unobserved entries are likely to also display some unobserved homoplasy). The conceptual and practical aspects of all these problems, as well as details of the implementation in TNT, are discussed.
TNT最近对隐含加权进行了若干扩展，能更好地处理结合形态学和分子数据集的数据集，以及那些包含大量缺失条目的数据集（例如古生物学矩阵，或某些分类群测序基因较少的组合矩阵）。由于最近有人建议，使用等权重（而非隐含加权）可能能更好地分析分子矩阵，因此提出了一种简单方法，即仅对某些性状（如形态学性状）应用隐含加权，而让其他性状具有恒定权重（如分子性状）。新方法还允许根据平均同塑性对整个分区进行加权，使分区中的每个性状具有相同权重（这可用于动态加权，例如整个基因，或第一、第二和第三密码子位置的集合）。这种方法在连续加权等方案中很容易实现，但在隐含加权的情况下会带来一些特殊问题。该方法有一个特殊含义，即包含无信息性状会影响结果（通过影响分区的隐含权重）。最后，对于具有许多缺失条目的性状可能会获得人为夸大的权重这一担忧（因为它们必然显示出较少的同塑性），可以通过允许对不同性状使用不同加权函数来解决，使得对于缺失条目较多的性状，额外转换的成本下降得更快（从而有效地假设未观察到的条目也可能显示出一些未观察到的同塑性）。本文讨论了所有这些问题的概念和实际方面，以及TNT中的实现细节。
