desDA: Descriptive Discriminant Analysis

In DiscriMiner: Tools of the Trade for Discriminant Analysis

Description

Performs a Descriptive Discriminant Analysis (a.k.a. Factorial Discriminant Analysis from the french Analyse Factorielle Discriminante)

Usage

  desDA(variables, group, covar = "within")

Arguments

variables	matrix or data frame with explanatory variables
group	vector or factor with group memberships
covar	character string indicating the covariance matrix to be used. Options are "within" and "total"

Details

When covar="within" the estimated pooled within-class covariance matrix is used in the calculations.
When covar="total" the total covariance matrix is used in the calculations.
The difference between covar="within" and covar="total" is in the obtained eigenvalues.

The estiamted pooled within-class covariance matrix is actually the within-class covariance matrix divided by the number of observations minus the number of classes (see getWithin)

Value

An object of class "desda", basically a list with the following elements

power	table with discriminant power of the explanatory variables
values	table of eigenvalues
discrivar	table of discriminant variables, i.e. the coefficients of the linear discriminant functions
discor	table of correlations between the variables and the discriminant axes
scores	table of discriminant scores for each observation

Author(s)

Gaston Sanchez

References

Lebart L., Piron M., Morineau A. (2006) Statistique Exploratoire Multidimensionnelle. Dunod, Paris.

See Also

discPower

Examples

## Not run: 
  # load bordeaux wines dataset
  data(bordeaux)

  # descriptive discriminant analysis with within covariance matrix
  my_dda1 = desDA(bordeaux[,2:5], bordeaux$quality)
  my_dda1

  # descriptive discriminant analysis with total covariance matrix
  my_dda2 = desDA(bordeaux[,2:5], bordeaux$quality, covar="total")
  my_dda2

  # plot factor coordinates with ggplot
  library(ggplot2)
  bordeaux$f1 = my_dda1$scores[,1]
  bordeaux$f2 = my_dda1$scores[,2]
  ggplot(data=bordeaux, aes(x=f1, y=f2, colour=quality)) +
  geom_hline(yintercept=0, colour="gray70") +
  geom_vline(xintercept=0, colour="gray70") +
  geom_text(aes(label=year), size=4) +
  opts(title="Discriminant Map - Bordeaux Wines (years)")
 
## End(Not run)

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