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Within-class MCA, also called conditional MCA
wcMCA(data, class, excl = NULL, row.w = NULL, ncp = 5)An object of class speMCA, with an additional item :
the within-class inertia percentage
.
data frame with only categorical variables, i.e. factors
factor specifying the class
numeric vector indicating the indexes of the "junk" categories (default is NULL). See getindexcat or use ijunk interactive function to identify these indexes. It may also be a character vector of junk categories, specified in the form "namevariable.namecategory" (for instance "gender.male").
numeric vector of row weights. If NULL (default), a vector of 1 for uniform row weights is used.
number of dimensions kept in the results (by default 5)
Nicolas Robette
Within-class Multiple Correspondence Analysis is a MCA where the active categories are centered on the mean of their class (i.e. conditional frequencies) instead of the overall mean (i.e. marginal frequencies).
It is also known as "conditional MCA" and can be seen as a special case of MCA on orthogonal instrumental variables, with only one (categorical) instrumental variable.
Escofier B., 1990, Analyse des correspondances multiples conditionnelle, La revue de Modulad, 5, 13-28.
Lebart L., Morineau A. et Warwick K., 1984, Multivariate Descriptive Statistical Analysis, John Wiley and sons, New-York.)
MCAoiv, wcPCA, PCAoiv
# within-class analysis of tea data
# with SPC as class
library(FactoMineR)
data(tea)
res <- wcMCA(tea[,1:18], tea$SPC)
res$ratio
ggcloud_variables(res)
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