geoDA

0th

Percentile

Geometric Predictive Discriminant Analysis

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
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.

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

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

Aliases
  • geoDA
Examples
## Not run: 
#   # 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
#   ## End(Not run)
Documentation reproduced from package DiscriMiner, version 0.1-29, License: GPL-3

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