within.pca {ade4} | R Documentation |
performs a normed within Principal Component Analysis.
within.pca(df, fac, scaling = c("partial", "total"), scannf = TRUE, nf = 2)
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 |
returns a list of the sub-class within
of class dudi'
. See within
Daniel Chessel chessel@biomserv.univ-lyon1.fr
Anne B Dufour dufour@biomserv.univ-lyon1.fr
Bouroche, J. M. (1975) Analyse des données ternaires: la double analyse en composantes principales. Thèse de 3ème cycle, Université de Paris VI.
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)