R: Multiple Correspondence Analysis

# lib(s): ca, rgl

# Red indicates stuff ya gotta to edit.

# Data input: generic

cat.infile <- data.frame("infile$cat.var1,"infile$cat.var2","infile$cat.var3",...,"infile$cat.var")

library(ca) # rgl will load automatically

# syntax is: mjca(obj, nd = 2, lambda = "adjusted", supcol = NA, subsetcol = NA, ps = "", maxit = 50, epsilon = 0.0001)
# approach to MCA is adjusted by lambda, where:
# lambda="indicator" - analysis based on simple correspondence analysis of the indicator matrix
# lambda="Burt" - analysis based on an eigen-decomposition of the Burt matrix
# lambda="adjusted" - analysis based on the Burt matrix with an adjustment of inerplot(Mtias
# lambda="JCA" - Joint Correspondence Analysis
# lib(s): ca, rgl

library(ca) # rgl will load automatically

MCA <- mjca(cat.infile) # where cat.infile is a bunch categorical variables (coded with dummy values???)
summary(MCA)
# how are the results visualized????
plot(MCA)
plot(MCA, mass = TRUE, contrib = "absolute", map ="rowgreen", arrows = c(FALSE, TRUE)) # asymmetric map

# /* END OF MULTIPLE CORRESPONDENCE ANALYSIS */