Multiple Correspondence Analysis (MCA) method

This function produces a list of elements to be used for MCA biplot construction.

Usage

MCA(bp, dim.biplot = c(2,1,3), e.vects = 1:ncol(bp$Xcat), 
       variant = "Indicator", lvl.num = TRUE)

Arguments

bp

object of class biplot obtained from preceding function biplot(center = FALSE).

dim.biplot

dimension of the biplot. Only values 1, 2 and 3 are accepted, with default 2.

e.vects

which eigenvectors (canonical variates) to extract, with default 1:dim.biplot.

variant

which multiple correspondence analysis variant, with default an only option in the current version being "Indicator", SVD performed on the weighted indicator matrix..

lvl.num

logical value to specify the labels of the category levels, with default TRUE to indicate levels with numbers (V1, V2, etc.). Alternatively the level names are used.

Value

A list with the following components is available:

Z

Combined data frame of the sample and category level coordinates.

Zprin

Sample coordinates (principal coordinates).

CLPstan

Category level point coordinates (standard coordinates).

CLPnames

Labels of the category level points as determined by lvl.num specification.

g

the number of groups for MCA will always be 2.

g.names

the group names refer to samples and variables in MCA

group.aes

the vector of category levels for the grouping variable. This is to be used for colour, pch and cex specification.

dim.biplot

The dimension of the biplot.

e.vects

Depending on what was specified in MCA argument.

See also

biplot()

Examples

biplot(as.data.frame(HairEyeColor)[,-4], center = FALSE) |> 
MCA(lvl.num = FALSE) |>  samples(col=c("purple3","forestgreen"), 
pch=c(15,17), label = TRUE, label.offset = 1, label.col="gray37") |> 
plot()