quaDA

0th

Percentile

Quadratic Discriminant Analysis

Performs a Quadratic Discriminant Analysis

Usage
quaDA(variables, group, prior = NULL, validation = NULL, learn = NULL, test = NULL, prob = FALSE)
Arguments
variables
matrix or data frame with explanatory variables
group
vector or factor with group memberships
prior
optional vector of prior probabilities. Default prior=NULL implies group proportions
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
prob
logical indicating whether the group classification results should be expressed in probability terms
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 "quada", basically a list with the following elements:
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.

Tenenhaus G. (2007) Statistique. Dunod, Paris.

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

See Also

classify, desDA, geoDA, linDA, plsDA

Aliases
  • quaDA
Examples
## Not run: 
#   # load iris dataset
#   data(iris)
# 
#   # quadratic discriminant analysis with no validation
#   my_qua1 = quaDA(iris[,1:4], iris$Species)
#   my_qua1$confusion
#   my_qua1$error_rate
# 
#   # quadratic discriminant analysis with cross-validation
#   my_qua2 = quaDA(iris[,1:4], iris$Species, validation="crossval")
#   my_qua2$confusion
#   my_qua2$error_rate
# 
#   # quadratic discriminant analysis with learn-test validation
#   learning = c(1:40, 51:90, 101:140)
#   testing = c(41:50, 91:100, 141:150)
#   my_qua3 = quaDA(iris[,1:4], iris$Species, validation="learntest",
#       learn=learning, test=testing)
#   my_qua3$confusion
#   my_qua3$error_rate
#   ## End(Not run)
Documentation reproduced from package DiscriMiner, version 0.1-29, License: GPL-3

Community examples

Looks like there are no examples yet.