In order to enhance interpretation of two-way contingency tables (cross-classifications) derived from two hierarchical classifications, new indices are suggested to evaluate the relative contribution of nodes in either hierarchy to the nodes or to a partition of groups derived from the other hierarchy. Using these tools, cut-levels in both hierarchies can be found to define optimal partitions, and groups from both partitions can be associated in order to identify their mutual relationships. The method is illustrated with an actual example from vegetation ecology.
In the analysis of multidimensional ecological data, it is often relevant to identify groups of variables since these groups may reflect similar ecological processes. The usual approach, the application of well-known clustering procedures using an appropriate similarity measure among the variables, may be criticized, but specific methods for clustering variables are neither investigated in detail nor used broadly. Here we introduce a new clustering method, the Hierarchical Factor Classification of variables, which is based on the evaluation of the least differences among representative variables of groups, as revealed by a two-dimensional Principal Components Analysis. As an additional feature, the method gives at each step a principal plane where both the grouped variables and the units, considered only according to these variables, can be projected. This method can be adapted to count data, so that it may be used for classifying both rows and columns of a contingency data table, by using the chi-square metric. In an example, we apply both methods to vegetation and soil data from the Campos in Southern Brazil.