HairColor


The sample data corresponds to 7 people and shows some demographic
  characteristics.


datasets

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.

Julio Cesar Hernandez Sanchez

NominalLogisticBiplot

Biplot representations of categorical data

HairColor function

This data frame contains 7 observation for the following 5 columns:
  <dl>
    <dt>Sex</dt><dd>two levels (M=male,F=female)</dd> <dt>HairColor</dt><dd>four levels of hair color (Dark, Grey, Fair and Brown)</dd> <dt>Region</dt><dd>(E = England,S = Scotland, W = Wales)</dd> <dt>Work</dt><dd>(Manual,Clerical,Professional)</dd> <dt>Education</dt><dd>(School,Univ,Postgrad)</dd> </dl> 

Format

Gower, J., Gardner-Lubbe,S., Le Roux,N. (2011). &#147;Understanding Biplots.&#148; <em>Wiley</em>.

HairColor: Demographic Data

Description

Usage

Arguments

Format

Source

Examples