within.pca {ade4} | R Documentation

## Normed within Principal Component Analysis

### Description

performs a normed within principal component analysis.

### Usage

    within.pca(df, fac, scaling = c("partial", "total"),
        scannf = TRUE, nf = 2)

### Arguments

`df` | a data frame with quantitative variables
`fac` | a factor distributing the rows of df in classes
`scaling` | a string of characters as a scaling option : if "partial", for each factor level, the sub-array is centred and normed. If "total", for each factor level, the sub-array is centred and the total array is then normed.
`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

### Value

returns a list of the sub-class `within` of class `dudi'`. See `within`

### Author(s)

Daniel Chessel chessel@biomserv.univ-lyon1.fr

### References

Bouroche, J. M. (1975) Analyse des données ternaires: la double analyse en composantes principales. Thèse de 3ème cycle, Université de Paris VI.

### Examples

    data(meaudret)
    wit1 <- within.pca(meaudret$mil, meaudret$plan$dat,
        scan = FALSE, scal = "partial")
    kta1 <- ktab.within(wit1, colnames = rep(c("S1","S2","S3","S4","S5"), 4))
    unclass(kta1)
    # See pta
    plot(wit1)

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