Normed within principal component analysis
Performs a normed within Principal Component Analysis.
withinpca(df, fac, scaling = c("partial", "total"),
scannf = TRUE, nf = 2)df |
a data frame with quantitative variables |
fac |
a factor partitioning the rows of df in classes |
scaling |
a string of characters as a scaling option : |
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 |
This functions implements the 'Bouroche' standardization. In a first
step, the original variables are standardized (centred and normed). Then, a second
transformation is applied according to the value of the scaling
argument. For "partial", variables are standardized in each sub-table
(corresponding to each level of the factor). Hence, variables have null
mean and unit variance in each sub-table. For "total", variables are
centred in each sub-table and then normed globally. Hence, variables
have a null mean in each sub-table and a global variance equal to one.
returns a list of the sub-class within of class dudi. See wca
Daniel Chessel
Anne-Béatrice Dufour anne-beatrice.dufour@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 <- withinpca(meaudret$env, meaudret$design$season, scannf = FALSE, scaling = "partial")
kta1 <- ktab.within(wit1, colnames = rep(c("S1", "S2", "S3", "S4", "S5"), 4))
unclass(kta1)
# See pta
plot(wit1)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.