What is the equivalent of e(sample) from Stata in R?

Question

I'm trying to replicate some functions from Stata in R, but I'm really really stuck with the e(sample) function after doing a multiple correspondence analysis (mca).

In Stata the code is this:

    clear

    set obs 10
    gen var1 = cond(_n <= 2, 0, 1)
    gen var2 = cond(_n == 1, 0, 1) 
    gen var3 = var2     

    mca var1 var2 var3, method(burt)
    predict var4 if e(sample)

The last command generates predicted values only for the observations used by mca.

In R, I have been doing this to do mca:

    if(!require("FactoMineR")) {
    install.packages("FactoMineR")
    library("FactoMineR") 
    }


    if(!require("factoextra")) {
    install.packages("factoextra")
    library("factoextra")
    }

    var1 <- c(0, 0, 1, 1, 1, 1, 1, 1, 1, 1)
    var2 <- c(0, 1, 1, 1, 1, 1, 1, 1, 1, 1)
    var3 <- c(0, 1, 1, 1, 1, 1, 1, 1, 1, 1)

    df <- data.frame(var1, var2, var3)


    df$var1 <- as.factor(df$var1)
    df$var2 <- as.factor(df$var2)
    df$var3 <- as.factor(df$var3)

    mca4 <- MCA(df, ncp = 2, method = "Burt")
    mca4$call$marge.col

And I get the same results from the mca process as in Stata, but I've not been able to replicate the last line from the Stata code predict var4 if e(sample), I already tried with predict.mca but it doesn't work at all: it gives me values from the dimensions specified in ncp = 2, so I guess it doesn't do the same as the predict command from Stata.

The results from Stata:

mca var1 var2 var3, method(burt)

Statistics for column categories in standard normalization

             |          Overall          |        Dimension_1        
  Categories |    Mass  Quality   %inert |   Coord   Sqcorr  Contrib 
-------------+---------------------------+---------------------------
var1         |                           |                           
           0 |   0.067    1.101    0.188 |   1.786    1.101    0.213 
           1 |   0.267    1.101    0.047 |  -0.446    1.101    0.053 
-------------+---------------------------+---------------------------
var2         |                           |                           
           0 |   0.033    0.936    0.344 |   3.148    0.936    0.330 
           1 |   0.300    0.936    0.038 |  -0.350    0.936    0.037 
-------------+---------------------------+---------------------------
var3         |                           |                           
           0 |   0.033    0.936    0.344 |   3.148    0.936    0.330 
           1 |   0.300    0.936    0.038 |  -0.350    0.936    0.037 
---------------------------------------------------------------------

predict var4 if e(sample)

The results of the predict command:

var4
2.912461
.3913612
-.4129778
-.4129778
-.4129778
-.4129778
-.4129778
-.4129778
-.4129778
-.4129778