  Classical Biplot Methods allow for the simultaneous representation of
individuals (rows) and variables (columns) of a data matrix. For Binary data,
Logistic biplots have been recently developed.When data are nominal, linear or
even binary logistic biplots are not adequate and techniques as Multiple
Correspondence Analysis (MCA), Latent Trait Analysis (LTA) or Item Response
Theory for nominal items should be used instead. In this paper we extend the
binary logistic biplot to nominal data. The resulting method is termed Nominal
Logistic Biplot, although the variables are represented as convex prediction
regions rather than vectors. 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. We
study the geometry of such a representation and construct computational
algorithms for the estimation of parameters and the calculation of prediction
regions. Nominal Logistic Biplots extend both MCA and LTA in the sense that
gives a graphical representation for LTA similar to the one obtained in MCA.
