Hierarchical Factor Classification of Variables in Ecology

In the analysis of multidimensional ecological data it is often relevant to identify groups of variables, for these groups may reflect similar ecological processes. The usual approach, that is applying well-known clustering procedures to an appropriate similarity measure among the variables, may be criticized, but specific methods for clustering variables are neither largely investigated nor broadly used. Here we introduce a new clustering method for Hierarchical Factor Classification of variables, which is based on the evaluation of the least differences among representative variables of groups, as seen by a two-dimensional Principal Components Analysis. As an additional feature the method gives at each step a principal plane where both grouped variables and units, as seen only by 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 on vegetation and soil data from Campos in South Brazil

Hierarchical factor classification of variables in 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