Thanks Nick,
No it's one course per plot but lots of such plots to
be produced. Anyway, while I was waiting for responses
I tried to find a solution and I ended up with
something that looks similar to your solution (I used
the vtokenize to ... tokenize names)
sum a
local ma=r(max)
sum b
local mb=r(max)
sum c
local mc=r(max)
vtokenize names
local i=1
while(`i'<=real(r(tokens))){
gen pos`i'=max(`ma',`mb',`mc')+`i'/4
local i=`i'+1
}
sc a b c Y,xlabel( ,val ) c(l l l ) || sc pos1
Y,ms(i) mlabpos(0) mlabel(names_1) xscale(range(0.5
6.5)) /*
*/ || sc pos2 Y,ms(i) mlabpos(0) mlabel(names_2) /*
*/ || sc pos3 Y,ms(i) mlabpos(0) mlabel(names_3) /*
*/ || sc pos4 Y,ms(i) mlabpos(0) mlabel(names_4) /*
*/legend(order(1 2 3) row(1))
The only problem now is that I don't know the number
of instructors a priori thus the number of additional
scatter commands (sc pos* Y) must be manually modified
for each course-dataset-plot. I used the returned
result r(tokens) to generate the positions but I can't
figure out how to deal with the multiple scatter
commands.
Thanks again for your help
Nikos Pantazis
Biostatistician
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