[R] Pls help to prevent my post from being indexed on google

Joshua Wiley jwiley.psych at gmail.com
Sun Apr 21 21:32:46 CEST 2013


Hi,

No that is not really possible for several reasons.  One is that these
posts are not archived on just one web server.  There are multiple
archives managed by different people/places.  Another is that they are
actually scrubbed to plain text, so my guess is that bots would miss
that tag anyway once it is in plain text (but I could be wrong).
Finally, most people when they reply quote previous messages (in fact
this is considered quite courteous so there is a record).  All these
things lead to the fact that when you post on a high volume public
list serv, your posts tend to be, well, public.

Best of luck to you,

Joshua

On Sun, Apr 21, 2013 at 9:54 AM, - Boon Loong <boon_loong at hotmail.com> wrote:
> Hi is it possible to add this line to my earlier post <meta name="robots" content="noindex"> to prevent it from being indexed by google? the post is at https://stat.ethz.ch/pipermail/r-help//2013-April/350857.html
> From: boon_loong at hotmail.com
> To: r-help at r-project.org
> Subject: Help for bootstrappingş
> Date: Thu, 4 Apr 2013 15:14:05 +0800
>
>
>
>
> 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
>         [[alternative HTML version deleted]]
>
>
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--
Joshua Wiley
Ph.D. Student, Health Psychology
University of California, Los Angeles
http://joshuawiley.com/
Senior Analyst - Elkhart Group Ltd.
http://elkhartgroup.com



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