disqual: Discriminant Analysis on Qualitative Variables
In gastonstat/DiscriMiner: Tools of the Trade for Discriminant Analysis

Description Usage Arguments Details Value Author(s) References See Also Examples

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

1 2	disqual(variables, group, validation = NULL, learn = NULL, test = NULL, autosel = TRUE, prob = 0.05)

`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`

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.

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

Gaston Sanchez

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

easyMCA, classify, binarize

## 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)