Interactive results exploration with explor

FactoMineR functions

Supported FactoMineR functions should work “out of the box”. Just pass the result object to explor().

Example with a principal correspondence analysis from FactoMineR::PCA :

library(FactoMineR)
data(decathlon)
pca <- PCA(decathlon[,1:12], quanti.sup = 11:12)
explor(pca)

Example with a simple correspondence analysis from FactoMiner::CA :

data(children)
res.ca <- CA(children, row.sup = 15:18, col.sup = 6:8)
explor(res.ca)

Example with a multiple correspondence analysis from FactoMineR::MCA :

library(FactoMineR)
data(hobbies)
mca <- MCA(hobbies[1:1000, c(1:8,21:23)], quali.sup = 9:10, 
           quanti.sup = 11, ind.sup = 1:100)
explor(mca)

ade4 functions

ade4 functions should also work by directly passing the object result to explor().

For example, to visualize a simple PCA results :

library(ade4)
data(deug)
pca <- dudi.pca(deug$tab, scale = TRUE, scannf = FALSE, nf = 5)
explor(pca)

There’s a bit more work to be done if you want to display supplementary elements, as ade4 don’t include them directly in the results analysis.

For a principal component analysis, you have to compute supplementary individuals (resp. variables) results with suprow (resp. supcol) and add them manually as a supi (resp. supv) element of your result object.

Here is an example of how to do this :

data(deug)
d <- deug$tab
sup_var <- d[-(1:10), 8:9]
sup_ind <- d[1:10, -(8:9)]
pca <- dudi.pca(d[-(1:10), -(8:9)], scale = TRUE, scannf = FALSE, nf = 5)
## Supplementary individuals
supi <- suprow(pca, sup_ind)
pca$supi <- supi$lisup
## Supplementary variables
supv <- supcol(pca, dudi.pca(sup_var, scale = TRUE, scannf = FALSE)$tab)
pca$supv <- supv$cosup
explor(pca)

You have to do the same thing for supplementary elements in a multiple correspondence analysis, with a slightly more complicated computation for supplementary variables :

data(banque)
d <- banque[-(1:100),-(19:21)]
ind_sup <- banque[1:100, -(19:21)]
var_sup <- banque[-(1:100),19:21]
acm <- dudi.acm(d, scannf = FALSE, nf = 5)
## Supplementary variables
acm$supv <- supcol(acm, dudi.acm(var_sup, scannf = FALSE, nf = 5)$tab)$cosup
colw <- acm$cw*ncol(d)
X <- acm.disjonctif(ind_sup)
X <- t(t(X)/colw) - 1
X <- data.frame(X)
## Supplementary individuals
acm$supi <- suprow(acm, X)$lisup
explor(acm)

For simple correspondence analysis, you can add supplementary rows or columns by adding their coordinates to supr and supc elements of your result object :

data(bordeaux)
tab <- bordeaux
row_sup <- tab[5,-4]
col_sup <- tab[-5,4]
coa <- dudi.coa(tab[-5,-4], nf = 5, scannf = FALSE)
coa$supr <- suprow(coa, row_sup)$lisup
coa$supc <- supcol(coa, col_sup)$cosup
explor(coa)