I have a question about taking random samples from my data. My dataset has
around 12,500 user ID's with 200,000 observations total and I want to take
around 500-600 (number of users) random samples. The problem is that each
member has multiple observations and I want to take all sub-observations for
each member. Each ID has 4 to 21 observations. For example, if ID number 5
has 10 observations, I want to take all 10 observations given ID number 5 is
included in the sample.
I tried the following and ended up with 580 number of users with around
8,800 observations. This method works, but I wonder if there is there any
better way for this job, because I have to drop duplicated samples with this
method.
gen idcnt=_N
bsample 600, cluster(id) /* sampling with replacement: I do not know how
to take cluster samples without replacement. */
bysort id: egen idcount=count(id)
compare idcount idcnt
duplicates tag, gen(dup)
drop if dup==1 /* To drop duplicated samples */
