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

[Joshua Wiley]

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