Structuring Interaction in Two-Way Tables by Clustering
Structuring Interaction in Two-Way Tables by Clustering
复制标题
通过聚类构建双向表中的交互
DOI：
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发表时间：
1990
期刊：
影响因子：
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通讯作者：
J. Denis
中科院分区：
文献类型：
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作者：
L. Corsten;J. Denis
提出了一种凝聚层次聚类方法，用于同时识别具有共同方差的不相关正态分布观测值的正交双向表中的非结构化行组和非结构化列组，使得行和列因子之间的相互作用仅归因于这些组之间的相互作用，从而得到比具有相互作用的完整模型更简约的模型。该过程基于交互作用分量的平方和，但在每个步骤中，交互作用的均方被用作行之间和列之间的邻近度量。如果方差的独立估计是可用的，停止规则是基于由Caliniski和Corsten（1985，Biometrics 41，39-48）提出的扩展的F比率同时检验程序。否则，一个近似的程序，包括方差估计建议。
An agglomerative hierarchical clustering procedure is presented for identifying simultaneously groups of unstructured rows and groups of unstructured columns in an orthogonal two-way table of uncorrelated normally distributed observations with common variance, such that the interaction between row and column factors is due only to interactions between those groups, leading to a more parsimonious model than the full model with interactions. The procedure is based on sums of squares for interaction components, but mean squares for interactions are used as proximity measure among rows and among columns in each step. If an independent estimate of the variance is available, a stopping rule is based on an extended F ratio simultaneous test procedure as proposed by Caliniski and Corsten (1985, Biometrics 41, 39-48). Otherwise, an approximate procedure including variance estimation is suggested.