DiscriMiner (version 0.1-28)

geoDA: Geometric Predictive Discriminant Analysis

Description

Performs a Geometric Predictive Discriminant Analysis

Usage

geoDA(variables, group, validation = NULL, learn = NULL,
    test = NULL)

Arguments

variables

matrix or data frame with explanatory variables

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

Value

• An object of class "geoda", basically a list with the following elements:
• functions
  table with discriminant functions
• 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.

Tuffery S. (2011) Data Mining and Statistics for Decision Making. Wiley, Chichester.

See Also

classify, desDA, linDA, quaDA, plsDA

Examples

# load bordeaux wines dataset
  data(iris)

  # geometric predictive discriminant analysis with no validation
  my_geo1 = geoDA(iris[,1:4], iris$Species)
  my_geo1$confusion
  my_geo1$error_rate

  # geometric predictive discriminant analysis with cross-validation
  my_geo2 = geoDA(iris[,1:4], iris$Species, validation="crossval")
  my_geo2$confusion
  my_geo2$error_rate