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| From | robert hartman <rohartman@gmail.com> |
| To | statalist@hsphsun2.harvard.edu |
| Subject | Re: st: Doing something an observation-specific number of times |
| Date | Tue, 28 Aug 2012 14:52:07 -0400 |
Austin, Thanks for your persistence. Your "v3" produces the same value as Maarten's "value." Many thanks. Sincerely, Rob On Tue, Aug 28, 2012 at 2:12 PM, Austin Nichols <austinnichols@gmail.com> wrote: > robert hartman <rohartman@gmail.com>: > Except I notice now you are dividing the ones by 2 as well, so > g v3=v2/2+v1*(1-v1^v2)/(1-v1)/2 > > On Tue, Aug 28, 2012 at 2:11 PM, Austin Nichols <austinnichols@gmail.com> wrote: >> robert hartman <rohartman@gmail.com>: >> In your example: >> v3=((1+(.41^1))/2) + ((1+(.41^2))/2) ...((1+(.41^77))/2) + ((1+(.41^78))/2) >> for v1=.41 and v2=.78 >> the sum is v2 (all the ones) plus a geometric series >> that sums to .5*.41*(1-.41^78)/(1-.41), right? >> I.e. >> g v3=v2+v1*(1-v1^v2)/(1-v1)/2 >> >> On Tue, Aug 28, 2012 at 2:03 PM, robert hartman <rohartman@gmail.com> wrote: >>> Thanks for the pointers, Maarten and Austin. >>> >>> I don't believe this is a geometric series, since the ratio of >>> consecutive terms is not constant. But I may just be missing it. >>> >>> Maarten, the data sets can get well into the tens and perhaps hundreds >>> of thousands. Code like what you've provided looks promising, though >>> you are probably right that there is no computational free lunch. >>> >>> On Tue, Aug 28, 2012 at 1:39 PM, Maarten Buis <maartenlbuis@gmail.com> wrote: >>>> On Tue, Aug 28, 2012 at 6:45 PM, robert hartman wrote: >>>>> Imagine that observation 1 has v1 and v2 values of .41 and 78, >>>>> respectively. <snip> For example, for observation 1, the new obs 1 v3 >>>>> value=((1+(.41^1))/2) + ((1+(.41^2))/2) ...((1+(.41^77))/2) + >>>>> ((1+(.41^78))/2). >>>>> >>>>> I have begun to think of some klugy ways of doing this via looping or >>>>> even the expand command. >>>> >>>> Depending on the number of observations in your original dataset the >>>> -expand- route may be the easiest. If the number of observations is >>>> large than this strategy may be infeasible due to memory limitations. >>>> When it comes to efficiency, you need to make the tradeoff between the >>>> amount of time you need to write the more fancy code (and the effort >>>> you will need to understand it again after some time...) against the >>>> time you safe because it runs quicker. Often the balance will be >>>> against the more fancy solutions(*). >>>> >>>> *---------------- begin example --------------- >>>> // create some example data >>>> clear >>>> input v1 v2 >>>> .41 78 >>>> .23 50 >>>> end >>>> >>>> // we need to keep track on who is who before >>>> // expanding >>>> gen id = _n >>>> >>>> // create v2 rows per observation >>>> expand v2 >>>> >>>> // create the appropriate exponent >>>> bys id : gen expo = _n >>>> >>>> // create the basic component of the computation >>>> gen double value = (1+v1^expo)/2 >>>> >>>> // sum() returns a running sum >>>> by id : replace value = sum(value) >>>> >>>> // the final sum is the last of the running sum >>>> bys id (expo) : replace value = value[_N] >>>> >>>> //get rid of things that are no longer needed >>>> drop expo >>>> by id : keep if _n == 1 >>>> drop id >>>> >>>> // see the result >>>> list >>>> *----------------- end example ---------------- >>>> (For more on examples I sent to the Statalist see: >>>> http://www.maartenbuis.nl/example_faq ) >>>> >>>> Hope this helps, >>>> Maarten >>>> >>>> (*) This of course ignores the pure joy you will get from figuring out >>>> the fancy solution, but we are not payed to enjoy ourselves! >>>> > * > * For searches and help try: > * http://www.stata.com/help.cgi?search > * http://www.stata.com/support/statalist/faq > * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/