I work with 4 sequence datasets that I want to compare. The datasets range from roughly 150 to 2000 sequences. I've been able to use TraMineR on datasets up to 1000 sequences long, but when I try to run the dataset with 2000 sequences, even our University's supercomputer cannot finish in under 36h. Hence, I'm considering sampling by id variable. My dataset looks like this:
id event time 1 edit 1 1 edit 2 2 comment 3 3 comment 4 4 close 5
Now, I want to take a random sample of, say, 2, by id numbers. Let's say the random sample yields id 1 and 4, then my sample would look like this:
id event time 1 edit 1 1 edit 2 4 close 5
In other words, I'm sampling 2 id numbers, but I'm actually getting 3 cases.
Now, on to my question: I only need to do this sampling for 1 of my 4 datasets, and I'm doing it for computational reasons. What are the considerations that needs to go into the sampling design? For example:
- Should I draw an equally large sample from every dataset (by % or N?) even if I don't have any problems computing on the smaller datasets?
- How large should the sample be in order for me to be able to claim generalizability?
- Would it be OK to simply sample as many cases as I can handle from the largest dataset, then run the other datasets without sampling (since they are small enough to compute on as they are), and then make comparisons across all 3 datasets?