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
Anne B Dufour dufour@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)

[Package ade4 version 1.4-0 Index]