quaDA: Quadratic Discriminant Analysis

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

Description

Performs a Quadratic Discriminant Analysis

Usage

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quaDA(variables, group, prior = NULL, validation = NULL,
  learn = NULL, test = NULL, prob = FALSE, functions = 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

functions

logical indicating whether the discriminant functions should be computed explicitly and returned

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" validation 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:

functions

discriminant functions - a list with an element for each group which, in turn, is a list containing an upper triangular matrix containing the coefficients of the quadratict terms, a vector containing the coefficients of the linear terms and a constant

confusion

confusion matrix

scores

discriminant scores for each observation

classification

assigned class

error_rate

misclassification error rate

Author(s)

Gaston Sanchez

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

linDA

Examples

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  ## 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)

nclSLR documentation built on May 2, 2019, 5:17 p.m.

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