I have a set of data for US t-bill returns and US stock returns frm 1980-2012. I am trying to bootstrap the data and obtain the minimum variance portfolio and repeat this portfolio 1000 times. However I am unable to get the correct code function for the minimum variance portfolio. When I tried to enter Opt(OriData+1, 1, 5, 0), I get "error:subscript out of bounds" Please help! 
library("quadprog")
##############################Preparing for datarawdata = read.table("C:/Desktop/data.txt", header=T)Rf = rawdata[,1]US = rawdata[,2]data = data.frame(Rf,US)OriData = as.matrix(data)
##############################the GetBSData functionGetBSData<-function(data){x = 1:396s = sample(x,6,replace=T)bsdata = data[(s[1]):(s[1]+59),]	for (j in 2:6) {  		a = data[(s[j]):(s[j]+59),]  		bsdata = rbind(bsdata,a)	}return(bsdata)}
#set.seed(1234)#trial<-GetBSData(OriData)
##############################the Minimisation functionOpt<-function(data, horizon, col, lamda){TbillReturn<-numeric(30/horizon)USReturn<-numeric(30/horizon)for (x in 1: (30/horizon)){	TbillReturn[x]<-prod(data[(12*horizon*(x-1)+1):(12*horizon*(x-1)+12*horizon),col])-1	USReturn[x]<-prod(data[(12*horizon*(x-1)+1):(12*horizon*(x-1)+12*horizon),2])-1}Return<-cbind(TbillReturn,USReturn)MeanVec<-c(mean(TbillReturn),mean(USReturn))VCovMat<-cov(Return)#return(MeanVec, VCovMat)
a<-c(1,1)a<-cbind(a, diag(1,2))
WtVec<-solve.QP(Dmat=VCovMat*2, dvec= MeanVec*lamda,Amat=a,bvec=c(1,0,0),meq=1)
#return(MeanVec, VCovMat, WtVec$solution)return(WtVec$solution)}
#Opt(OriData+1, 1, 5, 0)
##############################set.seed(4114)bs=1000						###number of bootstrap samplesRegion<-5						###Region indecies, check above.lamdaseq<-seq(0,1,.05)				###the lamda sequence. currently from 0 to 1 by .05.
x<-numeric(bs*length(lamdaseq))		###w1<-matrix(x, bs, length(lamdaseq))		###To initialise the matrices.w5<-matrix(x, bs, length(lamdaseq))		###1, 5, 10 denote the horizon.w10<-matrix(x, bs, length(lamdaseq))	###
for (i in 1: bs){BSData<-GetBSData(OriData)+1j=1	for (lamda in lamdaseq){		w1[i,j]<-Opt(BSData, 1, Region, lamda)[1]		w5[i,j]<-Opt(BSData, 5, Region, lamda)[1]		w10[i,j]<-Opt(BSData, 10, Region, lamda)[1]		j=j+1	}
x<-numeric(length(lamdaseq)*9)		###To initialise the tabletable<-matrix(x, length(lamdaseq), 9)	###
for (k in 1:length(lamdaseq)){		#k:index for lamda
table[k,1]<-sort(w1[,k])[.05*bs]		###The first 3 cols are for 1-yr horizon.table[k,2]<-mean(w1[,k])			###From left to right: 5 percentile,table[k,3]<-sort(w1[,k])[.95*bs]		###mean, and 95 percentile.
table[k,4]<-sort(w5[,k])[.05*bs]		###table[k,5]<-mean(w5[,k])			###Col 4-6 are for 5-yr horizon.table[k,6]<-sort(w5[,k])[.95*bs]		###
table[k,7]<-sort(w10[,k])[.05*bs]		###table[k,8]<-mean(w10[,k])			###Col 7-9 are for 5-yr horizon.table[k,9]<-sort(w10[,k])[.95*bs]		###}}
table
TenMinusOne<-numeric(length(lamdaseq))FiveMinusOne<-numeric(length(lamdaseq))TenMinusFive<-numeric(length(lamdaseq))
for (p in 1:length(lamdaseq)){DiffVec<-w10[,p]-w1[,p]TenMinusOne[p]<-length(DiffVec[DiffVec>0])
DiffVec<-w5[,p]-w1[,p]FiveMinusOne[p]<-length(DiffVec[DiffVec>0])
DiffVec<-w10[,p]-w5[,p]TenMinusFive[p]<-length(DiffVec[DiffVec>0])}
diff<-cbind(FiveMinusOne,TenMinusOne)diff<-cbind(diff, TenMinusFive)sn<-seq(1, length(lamdaseq))f2<-cbind(sn, diff)f2
##############################################END 		 	   		  
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