Detecting latent taxa: Monte Carlo comparison of taxometric, mixture model, and clustering procedures
Detecting latent taxa: Monte Carlo comparison of taxometric, mixture model, and clustering procedures
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DOI：
10.2466/pr0.87.5.37-47
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发表时间：
2000-08-01
影响因子：
2.3
通讯作者：
Haslam, N
中科院分区：
文献类型：
--
作者：
Cleland, CM;Rothschild, L;Haslam, N
利用分类和非分类的人工数据集，对四种检测分类性的方法进行了蒙特卡罗评估。对数据集进行了两种Meehl的分类方法，即MAXCOV和MAMBAC， Ward的聚类分析方法与三次聚类标准相一致，以及潜在变量混合建模技术。分类方法和潜在变量混合模型的性能在检测分类性方面明显优于聚类分析。应用研究人员被敦促从更好的程序中进行选择，并执行一致性休息。
A Monte Carlo evaluation of four procedures for detecting taxonicity was conducted using artificial data sets that were either taxonic or nontaxonic The data sets were analyzed using two of Meehl's taxometric procedures, MAXCOV and MAMBAC, Ward's method for cluster analysis in concert with the cubic clustering criterion and a latent variable mixture modeling technique. Performance of the taxometric procedures and latent variable mixture modeling were clearly superior to chat of cluster analysis in detecting taxonicity. Applied researchers are urged to select from the better procedures and to perform consistency rests.