------------------- Version 2 of my problem improving the definition of what the optimal solution would be.
Dear all, I'm working on the following problem: Assume two datasets: Y, Y that represent the same physical quantity Q. Dataset X contains values of Q after an event A while dataset Y contains values of Q after an event B. In R X, Y are vectors of the same length, containing effectivelly a number of observations of Q in each state. Q is a continous variable. Now, the two datasets should ideally not have any range of overlapping values. That is max(x) << min (Y) but that is not the reality of the problem. there are usually overlaps, bigger or smaller. Now, what I want to do is the following: Suppose that we choose a value P so that. Any X <= P is understood as belonging to group X while any Y > P is understood as belonging to group Y. now any values of X > P or of Y <= P are wrongly understood as belonging to Y nad X effectively. Hence we have Xerr -- > Sum( X >P) and Yerror --> Sum(Y<=P). I want to solve this bivariate optimization problem where I want to at the same time minimize the error of X and Y for a given P. Ultimately the target is to optimize the value of P so that the errors of both X and Y are optimized. More specifically, the optimal solution is one where 1. The total error (Xerr + Yerr) is minimized 2. The values or Xerr and Yerr are balanced as much as possible, probably. Does any1 have some functions in mind that can help with parts of this problem ? It's not impossible to write the algorithm but it will take time and things like convergence and robustness need to be checked.... ! thank you for your help. Best regards, Marios Barlas [[alternative HTML version deleted]] ______________________________________________ [hidden email] mailing list -- To UNSUBSCRIBE and more, see 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. |
You probably ought to read the CRAN Optimization Task View. [1]
You should also read the Posting Guide mentioned at the bottom of every R-help email (e.g. no homework, use plain text email). You should also read some guides on asking questions online (e.g. [2][3][4]). [1] https://cran.r-project.org/web/views/Optimization.html [2] http://stackoverflow.com/questions/5963269/how-to-make-a-great-r-reproducible-example [3] http://adv-r.had.co.nz/Reproducibility.html [4] https://cran.r-project.org/web/packages/reprex/index.html (read the vignette) -- Sent from my phone. Please excuse my brevity. On January 20, 2018 3:43:32 AM PST, BARLAS Marios 247554 <[hidden email]> wrote: >------------------- Version 2 of my problem improving the definition of >what the optimal solution would be. >Dear all, > >I'm working on the following problem: > >Assume two datasets: Y, Y that represent the same physical quantity Q. >Dataset X contains values of Q after an event A while dataset Y >contains values of Q after an event B. > >In R X, Y are vectors of the same length, containing effectivelly a >number of observations of Q in each state. > >Q is a continous variable. > >Now, the two datasets should ideally not have any range of overlapping >values. That is > >max(x) << min (Y) > >but that is not the reality of the problem. there are usually overlaps, >bigger or smaller. > >Now, what I want to do is the following: > >Suppose that we choose a value P so that. > >Any X <= P is understood as belonging to group X while >any Y > P is understood as belonging to group Y. > >now any values of X > P or of Y <= P are wrongly understood as >belonging to Y nad X effectively. > >Hence we have Xerr -- > Sum( X >P) and Yerror --> Sum(Y<=P). > >I want to solve this bivariate optimization problem where I want to at >the same time minimize the error of X and Y for a given P. Ultimately >the target is to optimize the value of P so that the errors of both X >and Y are optimized. More specifically, the optimal solution is one >where > >1. The total error (Xerr + Yerr) is minimized >2. The values or Xerr and Yerr are balanced as much as possible, >probably. > >Does any1 have some functions in mind that can help with parts of this >problem ? It's not impossible to write the algorithm but it will take >time and things like convergence and robustness need to be checked.... >! > >thank you for your help. > >Best regards, >Marios Barlas > > [[alternative HTML version deleted]] > >______________________________________________ >[hidden email] mailing list -- To UNSUBSCRIBE and more, see >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. ______________________________________________ [hidden email] mailing list -- To UNSUBSCRIBE and more, see 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. |
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