quaDA: Quadratic Discriminant Analysis

In DiscriMiner: Tools of the Trade for Discriminant Analysis

Description

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

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

classify, desDA, geoDA, linDA, plsDA

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)

Example output

            predicted
original     setosa versicolor virginica
  setosa         50          0         0
  versicolor      0         48         2
  virginica       0          1        49
[1] 0.02
            predicted
original     setosa versicolor virginica
  setosa         50          0         0
  versicolor      0         48         2
  virginica       0          1        49
[1] 0.1
            predicted
original     setosa versicolor virginica
  setosa          6          4         0
  versicolor      0          9         1
  virginica       2          0         8
[1] 0.2333333

DiscriMiner documentation built on May 1, 2019, 10:32 p.m.