## 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

`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

```
> ### 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
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))
```
```>
>
>
>
```