About NKormanik

NKormanik · ‎05-01-2013

Outstanding, , masterfully done. I'd never have gotten it on my own.

NKormanik · ‎04-30-2013

Suppose we are faced with results of 100 races by 100 runners. Consider a subset of the results in the following spreadsheet: Order Finished Race 1 Race 2 Race 3 1 D E D 2 A D B 3 E * A 4 C B C 5 B C * A SAS procedure I'm trying to use shows an example with the data in a different form, as follows: Runner Order_1 Order_2 Order 3 A 2 * 3 B 5 4 2 C 4 5 4 D 1 2 1 E 3 1 * As can be seen, some runners miss certain races. The above transformation was easy enough to do manually. But 100 x 100 is another story. Could one of you please help with the SAS code that will transform from the first format to the second? Your help will be greatly appreciated. By the way, the SAS example I am attempting to replicate using my data set is the following: Example 74.2 Principal Components of Basketball Rankings SAS/STAT(R) 12.1 User's Guide Thanks much! Nicholas Kormanik

NKormanik · ‎04-30-2013

We still need the code for the original small test in the Proc PRINQUAL manner, to compare results with the other ways suggested. I read the Basketball example, and it appears to be very similar. The original data set would need to be transformed, though. Please include that as well in your code. The approaches suggested by and by yielded the same results. Thanks to for suggesting PCA to begin with. Yes there are many different methodologies for determining rankings in sports. In the present circumstance, however, we are given this particular set of results, and must try to come up with the fairest conclusions. Particular concerns include: -- Ties. In a tie between S (Strong) and W (Weak), if S beat better opponents than W, seems the tie should be broken in the favor of S. -- Individuals missing some races, though in the races he/she participated in did very well.

NKormanik · ‎04-29-2013

Thanks very much for the lead. Tonight I'll be reading up on PROC PRINQUAL. Now what's needed here is to come up with the SAS code using PROC PRINQUAL to solve the initial problem. I'm sure the same result will be had for this small test. Will be very interesting, though, to see how it works with the larger data set.

NKormanik · ‎04-28-2013

Glad to see you back, PGStats. What is the significance of "not trivial to compute"? Does that mean this will work for small data sets, but not large ones? (Worked just fine, by the way -- copied and pasted directly into SAS. Same result as Reeza's way.) Seems the key improvement would be in solving the issue of ties. By the way, if any runner misses one race does that mean he's screwed for the series? Do you have a view on the PCA approach Ksharp mentioned? Thanks much!

NKormanik · ‎04-27-2013

Ksharp, why PCA as opposed to what Reeza has done? Would you please write the complete code for the simple example above so one can compare? Reeza's code seems to work flawlessly (copy and paste right into SAS). It would be interesting to see if PCA comes up with a different result.

NKormanik · ‎04-27-2013

Reeza, expertly done. Thank you so very much. I'll leave the question unanswered until tomorrow night in case anyone else has something more to add. For now, though, you're the champ!

NKormanik · ‎04-26-2013

Suppose we have 100 runners. And they run 100 races. All we are given is the order that the racers come in, 1 through 100. At the end of the race series what would be the fairest way to determine the top five winners? What SAS procedure might one use to arrive at the solution? We have 100 lists, of results. All lists numbered 1 to 100. A different ordering of names on each list, depending on the place they finish each race. We concatenate horizontally and come up with one large spreadsheet. Here would be a small sample of the spreadsheet: Order Finished Race 1 Race 2 Race 3 1 D A D 2 A D B 3 C B A 4 B C C The whole spreadsheet is 100 x 100. Let's assume that all runners run in all races. Any help and insights greatly appreciated. Nicholas Kormanik

NKormanik · ‎04-16-2013

_xcellent. I'll give it a try, Haikuo.

NKormanik · ‎04-16-2013

Thanks PG. Your first example would be my present case. I have a follow-up on this. Please advise: If I have many columns of data and not just one, and I want to do the same to each and every column, is there a way to do that? If there is not a shortcut way of changing more than one of the, say, 100 variables, the following is, I suppose, what I'd have to do. data want; call streaminit(7567); set have; x1 = x1 + (rand("UNIFORM")-0.5) * 0.00001; x2 = x2 + (rand("UNIFORM")-0.5) * 0.00001; x3 = x3 + (rand("UNIFORM")-0.5) * 0.00001; x4 = x4 + (rand("UNIFORM")-0.5) * 0.00001; x5 = x5 + (rand("UNIFORM")-0.5) * 0.00001; x6 = x6 + (rand("UNIFORM")-0.5) * 0.00001; . . . x100 = x100 + (rand("UNIFORM")-0.5) * 0.00001; run;

NKormanik · ‎04-15-2013

I'm using Proc G3Grid. Unfortunately in my data set there are violations of the following: "If more than one observation in the input data set has the same values for both horizontal variables, x and y, a warning message is printed, and only the ﬁrst such point is used in the interpolation." Therefore, I'd like to add a teeny tiny bit of randomness to those variables. For instance, if the original value is 5, I'd like to change that to 5.0000014738. etc. Can someone please show the appropriate code to do this? I'd like the changes to be "in place," as opposed to creating new variables. Thanks much. Nicholas Kormanik

NKormanik · ‎04-13-2013

After experimentation, seems memory usage and time needed per iteration is HUGELY contingent upon the size of the data set. If the data set is small -- around 5000 observations or less -- SAS will chug along and get the job done. A few minutes per iteration. No choice, then, but to sub-divide data set. Curious to have access to SAS on a university mainframe, and see what happens. Would the entire thing be done in a flash??

NKormanik · ‎04-13-2013

Not sure about fullstimer. But, I cut my total data file to one-tenth (10%) of its size -- from 100,000 observations, to 10,000 observations -- and SAS was able to run the above program. However each iteration takes 20 minutes!! With 16,000 iterations needed. Memsize in SAS Options Environment > System administration > Memory > Memsize = 2147483648 Memory in computer is 4Gb. Windows 7, 64-bit. SAS 9.3, 32-bit. So..., the best suggestion seems to be to rewrite the program??

NKormanik · ‎04-12-2013

Here's the code for the first iteration: proc g3grid data=nicholas.data out=nicholas._20101_20102_ ; grid '20101'n * '20102'n = '50501'n '50502'n '50503'n '50504'n '50505'n '50508'n '50401'n '50402'n '50403'n '50404'n '50405'n '50408'n / spline smooth = .05 naxis1 = 21 naxis2 = 21 ; run ; quit ; data nicholas._20101_20102_ ; set nicholas._20101_20102_ ; Combo = '20101_20102' ; label '20101'n = Var_1 ; label '20102'n = Var_2 ; rename '20101'n = Var_1 ; rename '20102'n = Var_2 ; run ; quit ; data nicholas._20101_20102_ ; retain Combo ; set nicholas._20101_20102_ ; run ; quit ; ===== There are 16,000 of such iterations in total. Having a new session for every, say, 3 iterations would be a lot of work. I'm hoping there will be some easier way. One possibility I'm going to try is to make the data set smaller.

NKormanik · ‎04-12-2013

The SAS program I'm running involves Proc G3Grid, then a Data step. These two repeat over and over, say, 100 times. This process gets through 3 iterations of the above just fine, no problems, then it can't do any more after 3. Here is the error message: ERROR: Memory allocation failure. ERROR: The SAS System stopped processing this step because of insufficient memory. Since 3 got done successfully, something seems to then get retained by the memory, and accumulates, until all memory is filled. Is it possible to put a command between the iterations to clear memory? Any other ideas you might have would be greatly appreciated. Thanks. Nicholas Kormanik

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