Conjunctive prediction of an ordinal criterion variable on the basis of binary predictors

In this paper we propose an empirical prediction method to retrieve, for a given ordinal criterion and a set of binary predictors, a series of nested sets of predictors, each set containing all singly necessary (and, if feasible, jointly sufficient) predictors for a particular criterion value. The method extends a previously developed approach to construct approximate Galois lattice models of binary data. After sketching an outline of the new model and associated algorithm we illustrate our method with an application to real psychological data on the experience of anger.