linDA: Linear Discriminant Analysis

In DiscriMiner: Tools of the Trade for Discriminant Analysis

Description

Performs a Linear Discriminant Analysis

Usage

  linDA(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" validation is performed by providing a learn-set and a test-set of observations.

Value

An object of class "linda", 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

Author(s)

Gaston Sanchez

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, geoDA, quaDA, plsDA

Examples

## Not run: 
  # load iris dataset
  data(iris)

  # linear discriminant analysis with no validation
  my_lin1 = linDA(iris[,1:4], iris$Species)
  my_lin1$confusion
  my_lin1$error_rate

  # linear discriminant analysis with cross-validation
  my_lin2 = linDA(iris[,1:4], iris$Species, validation="crossval")
  my_lin2$confusion
  my_lin2$error_rate

  # linear discriminant analysis with learn-test validation
  learning = c(1:40, 51:90, 101:140)
  testing = c(41:50, 91:100, 141:150)
  my_lin3 = linDA(iris[,1:4], iris$Species, validation="learntest", learn=learning, test=testing)
  my_lin3$confusion
  my_lin3$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] 0.02666667
            predicted
original     setosa versicolor virginica
  setosa         10          0         0
  versicolor      0         10         0
  virginica       0          0        10
[1] 0

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