DiscriMiner (version 0.1-29)

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

## Not run: 
#   # 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
#   ## End(Not run)