Hi everyone! I have a data frame with 1112 time series and I am going to randomly sampling r samples for z times to compose different portfolio size(r securities portfolio). As for r=2 and z=10000,that's: z=10000 A=seq(1:1112) x1=sample(A,z,replace =TRUE) x2=sample(A,z,replace =TRUE) M=cbind(x1,x2) # combination of 2 series Because in a portfolio with x1[i]=x2[i],(i=1,2,...,10000) means a 1 securities' portfolio,not 2 securities',it should be eliminated and resampling. With r increase, for example r=k, how do I efficiently eliminated all such portfolio as x1[i]=x2[i]=...=xk[i]?
Besides, any r securities' portfolio with the same securities' combination means the same portfolio(given same weights as here), e.g. M(x1[i],x5[i],x7[i],x1000[i]) and M(x5[i],x7[i],x1[i],x1000[i]) or M(x1[i],x7[i],x5[i],x1000[i]) are the same, how do I efficiently eliminat these possibilities? -- View this message in context: http://r.789695.n4.nabble.com/elimination-duplicate-elements-sampling-tp3652791p3652791.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ 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.
