A few weeks ago, I published a post on Visualising a Classification in High Dimension, based on the use of a principal component analysis, to get a projection on the first two components. Following that post, I was wondering what could be done in the context of a classification on categorical covariates. A natural idea would be to consider a correspondance analysis, and to run a similar code.
Consider here the dataset used in a recent post,
If we consider a correspondance analysis, we get
> library(FactoMineR) > acm=MCA(train.db,quali.sup = + which(names(train.db,)=="class"),ncp=10)
For the covariates (including also the variable we want to model, considered here as some supplementary variable), the visualisation – on the first two components – is
and for the individuals