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Assessing Congruence Among Ultrametric Distance Matrices

机译:评估超测距矩阵之间的一致性

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Recently, a test of congruence among distance matrices (CADM) has been developed. The null hypothesis is the incongruence among all data matrices. It has been shown that CADM has a correct type I error rate and good power when applied to independently-generated distance matrices. In this study, we investigate the suitability of CADM to compare ultrametric distance matrices. We tested the type I error rate and power of CADM with randomly generated dendrograms and their associated ultrametric distance matrices. We show that the test has correct type I error rates and good power. To obtain the significance level of the statistic, a single (as in the Mantel test) or a double (as in the double permutation test, DPT) permutation procedure was used. The power of CADM remained identical when the two permutation methods were compared. This study clearly demonstrates that CADM can be used to determine whether different dendrograms convey congruent information.
机译:最近,已经开发了距离矩阵(CADM)之间的一致性测试。零假设是所有数据矩阵之间的不一致。已经证明,将CADM应用于独立生成的距离矩阵时,具有正确的I型错误率和良好的功效。在这项研究中,我们调查了CADM比较超测距矩阵的适用性。我们使用随机生成的树状图及其相关的超距距离矩阵测试了I型错误率和CADM的功效。我们证明该测试具有正确的I型错误率和良好的功效。为了获得统计的显着性水平,使用了单个(如在Mantel检验中)或双重(如在双重置换检验中,DPT)置换过程。比较两种置换方法时,CADM的功能保持相同。这项研究清楚地表明,CADM可用于确定不同的树状图是否传达一致的信息。

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