Hi, I have a large dataset on course enrollment. Individual students take courses in different semesters. Observations are unique at the individual-semester-coursenum level. Individuals also have different graduation years ("cohort"). I would like to select, for each individual, a random sample of individuals in their cohort of a different size ("total") that is different for each individual. The best possible way I can think of is to loop through the individual observations and use the randomtag command, and create a unique identifer for each value of random tag (possibly the unique identifer of the student). For example, the following code works:
preserve
keep id cohort
duplicates drop /* We now have one observation per individual */
egen total = count(id), by (cohort)
local N=_N
set seed 1357
g randomgroup = .
sort id
forvalues i = 1/`N' {
global id = id[`i']
global year = cohort[`i']
global groupsize = total[`i']
randomtag if cohort == $year, count($groupsize) g(selected$id)
replace randomgroup = $id*selected$id if selected$id == 1
}
sort id
save randomgroups.dta
restore
sort id
merge id using randomgroups.dta
I'm wondering if there is a faster way to do this, rather than looping over individual observations to generate random samples one at a time. Thank you for your suggestions.
preserve
keep id cohort
duplicates drop /* We now have one observation per individual */
egen total = count(id), by (cohort)
local N=_N
set seed 1357
g randomgroup = .
sort id
forvalues i = 1/`N' {
global id = id[`i']
global year = cohort[`i']
global groupsize = total[`i']
randomtag if cohort == $year, count($groupsize) g(selected$id)
replace randomgroup = $id*selected$id if selected$id == 1
}
sort id
save randomgroups.dta
restore
sort id
merge id using randomgroups.dta
I'm wondering if there is a faster way to do this, rather than looping over individual observations to generate random samples one at a time. Thank you for your suggestions.
