Thanks!!! works perfectly. I'll use t(rmultinom(x, m, rep(1,n))/m) for now and look up in my stats book the formula for determining the number of sample sizes to achieve a given p level. The second question was more pertaining to how many combinations are too many to attempt to calculate every possibility.
I ran a few tests and it took about 1 min or so for R to run z <- subsets(15, 10, allow.repeat=T) which is ~ 2 million combinations. Based on that, I figure I'll sample when the number of combinations starts to approach 1 million. Thanks again. Reuben On Thu, Dec 11, 2008 at 11:02 PM, G. Jay Kerns <gke...@ysu.edu> wrote: > Reuben, > > On Thu, Dec 11, 2008 at 9:28 PM, Reuben Cummings <reub...@gmail.com> wrote: >> Good point, I actually thought about doing some kind of sampling >> before, but now I think I don't have a choice. Essentially, what I am >> doing is calculating possible asset allocation possibilities, e.g. >> >> N is the number of available assets and M is the available buckets of >> money to spend on the assets. With N = 3 and M = 2, I am saying I have >> a choice to buy any of 3 three assets and I can afford to make 2 such >> purchases. These combinations are represented below: >> >> M1 M2 >> [1,] 1 1 >> [2,] 1 2 >> [3,] 1 3 >> [4,] 2 2 >> [5,] 2 3 >> [6,] 3 3 >> >> [1,] is saying I spend all of my money buying asset 1. >> >> this produces the following asset allocations >> >> A1 A2 A3 >> [1,] 1 0 0 >> [2,] .5 .5 0 >> [3,] .5 0 .5 >> [4,] 0 1 0 >> [5,] 0 .5 .5 >> [6,] 0 0 1 >> >> [1,] again, is saying that my allocation is 100% asset 1. >> >> from here, I plan to run some risk/performance metrics on the >> allocations and then come up with a list of the best allocation >> choices. >> >> The sampling will come in handy to give me random asset allocations. >> I've taken a statistical design of experiments class, so I am familiar >> with generating factorial experiment designs. >> >> So, how would I go about producing the random samples? > > how about this: > > rmultinom(10, 2, rep(1,3))/2 > > Please note that the above assumes a model in which it is equally > likely to choose asset 1, 2, or 3. You should think about the model > carefully and adjust the "prob" argument accordingly. > >> And what # of >> combinations should I use as the cutoff for doing a sampling instead >> of testing every possible combination? > > There isn't a definitive answer to that question. Assuming that it is > desired to find an 'average risk' of some sort, it would be good to > increase the number of samples until the risk estimate stabilizes to a > value with which you are comfortable. > > Best, > Jay > > > > > > > *************************************************** > G. Jay Kerns, Ph.D. > Associate Professor > Department of Mathematics & Statistics > Youngstown State University > Youngstown, OH 44555-0002 USA > Office: 1035 Cushwa Hall > Phone: (330) 941-3310 Office (voice mail) > -3302 Department > -3170 FAX > E-mail: gke...@ysu.edu > http://www.cc.ysu.edu/~gjkerns/ > ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
