DiscriMiner (version 0.1-28)

disqual: Discriminant Analysis on Qualitative Variables

Description

Implementation of the DISQUAL methodology. Disqual performs a Fishers Discriminant Analysis on components from a Multiple Correspondence Analysis

Usage

disqual(variables, group, validation = NULL,
    learn = NULL, test = NULL, autosel = TRUE, prob = 0.05)

Arguments

variables

data frame with qualitative explanatory variables (coded as factors)

group

vector or factor with group memberships

validation

type of validation, either "crossval" or "learntest". Default NULL

learn

optional vector of indices for a learn-set. Only used when validation="learntest". Default NULL

test

optional vector of indices for a test-set. Only used when validation="learntest". Default NULL

autosel

logical indicating automatic selection of MCA components

prob

probability level for automatic selection of MCA components. Default prob = 0.05

Value

• An object of class "disqual", basically a list with the following elements:
• raw_coefs
  raw coefficients of discriminant functions
• norm_coefs
  normalizaed coefficients of discriminant functions, ranging from 0 - 1000
• confusion
  confusion matrix
• scores
  discriminant scores for each observation
• classification
  assigned class
• error_rate
  misclassification error rate

Details

When validation=NULL there is no validation When validation="crossval" cross-validation is performed by randomly separating the observations in ten groups. When validation="learntest" validationi is performed by providing a learn-set and a test-set of observations.

References

Lebart L., Piron M., Morineau A. (2006) Statistique Exploratoire Multidimensionnelle. Dunod, Paris.

Saporta G. (2006) Probabilites, analyse des donnees et statistique. Editions Technip, Paris.

Saporta G., Niang N. (2006) Correspondence Analysis and Classification. In Multiple Correspondence Analysis and Related Methods, Eds. Michael Greenacre and Jorg Blasius, 371-392. Chapman and Hall/CRC

See Also

easyMCA, classify, binarize

Examples

# load insurance dataset
  data(insurance)

  # disqual analysis with no validation
  my_disq1 = disqual(insurance[,-1], insurance[,1], validation=NULL)
  my_disq1

  # disqual analysis with cross-validation
  my_disq2 = disqual(insurance[,-1], insurance[,1], validation="crossval")
  my_disq2