DiscriMiner (version 0.1-29)

linDA: Linear 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

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

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.

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)