Dear Statalist,

this is a very reduced version of a much larger dataset:

Code:
* Example generated by -dataex-. To install: ssc install dataex
clear
input float id byte(f1 o3 o4 s1)
  1 1 1 0 0
  2 1 0 0 0
  3 1 0 0 0
  4 1 0 0 0
  5 1 0 0 0
  6 1 0 0 0
  7 1 0 0 0
  8 1 1 1 0
  9 1 0 0 0
 10 1 1 0 0
 11 1 1 1 1
 12 1 1 1 0
 13 1 1 1 0
 14 1 0 0 0
 15 1 0 0 0
 16 1 0 0 0
 17 1 0 0 0
 18 1 0 1 1
 19 1 0 0 0
 20 1 1 1 0
 21 1 0 0 0
 22 1 0 0 0
 23 1 0 0 0
 24 1 0 0 0
 25 1 1 0 0
 26 1 0 1 0
 27 1 0 1 0
 28 1 0 0 1
 29 1 0 1 0
 30 1 0 0 0
 31 1 0 0 0
 32 1 0 1 0
 33 1 1 1 0
 34 1 0 0 0
 35 1 1 1 0
 36 1 1 1 0
 37 1 0 0 0
 38 1 0 1 1
 39 1 0 0 0
 40 1 1 0 0
 41 1 1 1 0
 42 1 0 1 1
 43 1 0 0 0
 44 1 0 0 0
 45 1 1 0 0
 46 1 0 1 0
 47 1 0 0 0
 48 1 1 0 0
 49 1 1 1 0
 50 1 0 1 1
 51 1 1 1 0
 52 1 0 0 0
 53 1 0 1 0
 54 1 0 0 1
 55 1 1 1 0
 56 1 1 0 1
 57 1 0 0 1
 58 1 0 0 0
 59 1 1 0 1
 60 1 1 1 0
 61 1 0 1 1
 62 1 0 1 1
 63 1 1 0 1
 64 1 1 1 0
 65 1 1 1 1
 66 1 1 0 1
 67 1 1 1 0
 68 1 0 1 0
 69 1 1 1 1
 70 1 0 0 0
 71 1 1 0 0
 72 1 0 0 0
 73 1 0 0 0
 74 1 0 1 0
 75 1 0 0 0
 76 1 1 1 0
 77 1 0 0 0
 78 1 1 1 0
 79 1 1 0 0
 80 1 1 1 1
 81 1 1 0 0
 82 1 1 0 0
 83 1 0 1 0
 84 1 1 1 0
 85 1 1 1 0
 86 1 1 1 0
 87 1 0 0 0
 88 1 1 1 0
 89 1 0 0 0
 90 1 0 0 0
 91 1 1 0 1
 92 1 1 1 0
 93 1 1 0 0
 94 1 0 0 0
 95 1 1 0 1
 96 1 0 0 0
 97 1 0 1 0
 98 1 1 0 1
 99 1 0 0 0
100 1 0 0 0
end
label values f1 f1ex
label def f1ex 1 "vg", modify
label values o3 o3
label def o3 0 "nf", modify
label def o3 1 "f", modify
label values o4 o4
label def o4 0 "nm", modify
label def o4 1 "m", modify
label values s1 s1
label def s1 0 "nnaS", modify
label def s1 1 "natS", modify
I am performing joint correspondence analysis and visualise the results with mcaplot...
Code:
mca o3 o4 s1, supp(f1) method(joint)
mcaplot, overlay  xline(0) yline(0) scale(.5) aspectratio(1) legend(off) mlabpos(12) msymbol(circle) mlabgap(2) name(mca)
and a space of individuals / scatterplot of the observations:
Code:
predict a1 a2
scatter a2 a1,  xline(0) yline(0) scale(.3) mlabel(id) name(scat)
(it looks much less strange on my original dataset, I promise).

What I would like to do now is to combine both graphs so that I have both the variables and observations in one plot, similar to what can be done with the factoextra package in R.
But, e.g.
Code:
graph combine mca scat
gives me two seperate graphs.

Is it even possible to combine mca postestimation plots with a scatterplot?

Best,
Nadine