discrimin {ade4}R Documentation

Linear Discriminant Analysis (descriptive statistic)

Description

performs a linear discriminant analysis.

Usage

discrimin(dudi, fac, scannf = TRUE, nf = 2)
## S3 method for class 'discrimin':
plot(x, xax = 1, yax = 2, ...) 
## S3 method for class 'discrimin':
print(x, ...) 

Arguments


dudi a duality diagram, object of class dudi
fac a factor defining the classes of discriminant analysis
scannf a logical value indicating whether the eigenvalues bar plot should be displayed
nf if scannf FALSE, an integer indicating the number of kept axes
x an object of class 'discrimin'
xax the column number of the x-axis
yax the column number of the y-axis
... further arguments passed to or from other methods

Value

returns a list of class 'discrimin' containing :
nf a numeric value indicating the number of kept axes
eig a numeric vector with all the eigenvalues
fa a matrix with the loadings: the canonical weights
li a data frame which gives the canonical scores
va a matrix which gives the cosines between the variables and the canonical scores
cp a matrix which gives the cosines between the components and the canonical scores
gc a data frame which gives the class scores

Author(s)

Daniel Chessel
Anne B Dufour dufour@biomserv.univ-lyon1.fr

See Also

lda in package MASS

Examples

data(chazeb)
dis1 <- discrimin(dudi.pca(chazeb$tab, scan = FALSE), chazeb$cla, 
    scan = FALSE)
dis1
plot(dis1)

data(skulls)
plot(discrimin(dudi.pca(skulls, scan = FALSE), gl(5,30), 
    scan = FALSE))

Worked out examples


> library(ade4)
> ### Name: discrimin
> ### Title: Linear Discriminant Analysis (descriptive statistic)
> ### Aliases: discrimin plot.discrimin print.discrimin
> ### Keywords: multivariate
> 
> ### ** Examples
> 
> data(chazeb)
> dis1 <- discrimin(dudi.pca(chazeb$tab, scan = FALSE), chazeb$cla, 
+     scan = FALSE)
> dis1
Discriminant analysis
call: discrimin(dudi = dudi.pca(chazeb$tab, scan = FALSE), fac = chazeb$cla, 
    scannf = FALSE)
class: discrimin 

$nf (axis saved) : 1

eigen values: 0.8451

  data.frame nrow ncol content                          
1 $fa        6    1    loadings / canonical weights     
2 $li        23   1    canonical scores                 
3 $va        6    1    cos(variables, canonical scores) 
4 $cp        6    1    cos(components, canonical scores)
5 $gc        2    1    class scores                     

> plot(dis1)
> 
> data(skulls)
> plot(discrimin(dudi.pca(skulls, scan = FALSE), gl(5,30), 
+     scan = FALSE))
> 
> 
> 
> 

[Package ade4 Index]