Biplot representations of categorical data

Analysis of a matrix of polytomous items using Nominal Logistic Biplots (NLB) according to Hernandez-Sanchez and Vicente-Villardon (2013). The NLB procedure extends the binary logistic biplot to nominal (polytomous) data. The individuals are represented as points on a plane and the variables are represented as convex prediction regions rather than vectors as in a classical or binary biplot. Using the methods from Computational Geometry, the set of prediction regions is converted to a set of points in such a way that the prediction for each individual is established by its closest "category point". Then interpretation is based on distances rather than on projections. In this package we implement the geometry of such a representation and construct computational algorithms for the estimation of parameters and the calculation of prediction regions.


Reference manual

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0.2 by Julio Cesar Hernandez Sanchez, 7 years ago

Browse source code at

Authors: Julio Cesar Hernandez Sanchez , Jose Luis Vicente-Villardon

Documentation:   PDF Manual  

GPL (>= 2) license

Depends on mirt, gmodels, MASS

Depended on by OrdinalLogisticBiplot.

See at CRAN