On Thu, Mar 01, 2012 at 04:27:45AM -0800, syrvn wrote: > Hello, > > I am stuck with selecting the right rows from a data frame. I think the > problem is rather how to select them > then how to implement the R code. > > Consider the following data frame: > > df <- data.frame(ID = c(1,2,3,4,5,6,7,8,9,10), value = > c(34,12,23,25,34,42,48,29,30,27)) > > What I want to achieve is to select 7 rows (values) so that the mean value > of those rows are closest > to the value of 35 and the remaining 3 rows (values) are closest to 45. > However, each value is only > allowed to be sampled once!
Hi. If some 3 rows have mean close to 45, then they have sum close to 3*45, so the remaining 7 rows have sum close to sum(df$value) - 3*45 # [1] 169 and they have mean close to 169/7 = 24.14286. In other words, the two criteria cannot be optimized together. For this reason, let me choose the criterion on 3 rows. The closest solution may be found as follows. # generate all triples and compute their means tripleMeans <- colMeans(combn(df$value, 3)) # select the index of the triple with mean closest to 35 indClosest <- which.min(abs(tripleMeans - 35)) # generate the indices, which form the closest triple in df$value tripleInd <- combn(1:length(df$value), 3)[, indClosest] tripleInd # [1] 1 3 7 # check the mean of the triple mean(df$value[tripleInd]) # [1] 35 This code constructs all triples. If it is used for k-tuples for a larger k and for a set of n values, its complexity will be proportional to choose(n, k), so it will be large even for moderate n, k. It is hard to provide a significant speed up, since some variants of "knapsack problem", which is NP-complete, may be reduced to your question. Consequently, it is, in general, NP-complete. Hope this helps. Petr Savicky. ______________________________________________ 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.
