In medical research, continuous variables are often converted into categorical variables by grouping values into two or more categories. We consider in detail issues pertaining to creating just two groups, a common approach in clinical research. We argue that the simplicity achieved is gained at a cost; dichotomization may create rather than avoid problems, notably a considerable loss of power ...

This paper discusses the efficiency of Ritschard et al.’s heuristic [RIT 01] for simultaneously determining the optimal aggregation of two categorical variables. The heuristic is a bottom-up algorithm that proceeds successively to the grouping of two categories that maximizes the increase in the selected criteria. The heuristic is compared to the exhaustive scanning of all possible groupings. T...

In the behavioral sciences, response variables are often noncontinuous, common types being dichotomous, ordinal or nominal variables, counts and durations. Conventional structural equation models (SEMs) have thus been generalized to accommodate different kinds of responses.

In the late sixties F. W. LAWVERE showed that the logical connectives and quantifiers were examples of the categorical notion of adjointness. In [9] and [lo] he amplified this notion by a more thorough discussion of the structure of a hyperdoctrine, which had much of the flavour of intuitionistic logic with equality. In this eontext it was natural to “stratify” formulae and proofs according to ...

Centuries of chart-making have produced some outstanding charts tailored specifically to the data being visualized. They have also produced a myriad of less-than-outstanding charts in the same vein. I instead present a set of techniques that may be applied to arbitrary datasets with specific properties. In particular, I describe two techniques – Nested Category Maps and Correlation Maps – for v...

In supervised machine learning, the partitioning of the values (also called grouping) of a categorical attribute aims at constructing a new synthetic attribute which keeps the information of the initial attribute and reduces the number of its values. In this paper, we propose a new grouping method MODL founded on a Bayesian approach. The method relies on a model space of grouping models and on ...