MCA weights to construct a score

Question

I have a set of variables all measured on a nominal scale. I have applied the MCA function within the FactoMineR package to reduce the dimension of my data set. Next I would like to calculate a score for each participant. Here is an extract from the article:

The following equation was used to calculate a composite asset index score for each population unit (or household): MCAPi = Ri1W1 + Ri2W2 + … + RijWj + … + RiJWJ Where MCAPi is the ith household’s composite poverty indicator score, Rij is the response of household i to category j, and Wj is the MCA weight for dimension one applied to category j. MCA was employed to calculate these weights, using the mca command in Stata8 (Statacorp, 2003; Van Kerm, 1998). This command estimates “an adjusted simple correspondence analysis on the Burt matrix.

1. What are the MCA weights that they refer to? Is it the the column coordinates?
2. How do you extend the idea when you use for example three instead of only one dimension?

Small example:

library(FactoMineR)
library(factoextra)
a<-c(3,1,1,3,2)
b<-c(2,2,3,1,3)
example<-data.frame(a,b)
example <-example%>%mutate_if(is.numeric,as.factor)
res.mca <- MCA(example, graph = TRUE,method = "Burt")
res.mca$var$coord

Are res.mca$var$coord the MCA weights that the article is referring to?