Asher Meir <asher.m...@gmail.com> wrote on 12/30/2011 03:45:48 AM:
> Yes this is the solution to my problem.
>
> I think there is a small typo in old.index, instead of:
>
> k=rep(1:nks, rep(njs*nks, nks)))
>
> it should be:
> k=rep(1:nks, rep(nis*njs, nks)))
> That is, the "k" index should be replicated by nis*njs,, not nks*njs.
Right you are. Sorry about that.
> Many thanks
You're welcome.
Jean
> Asher
> On Fri, Dec 30, 2011 at 2:36 AM, Jean V Adams <jvad...@usgs.gov> wrote:
>
> Asher Meir <asher.m...@gmail.com> wrote on 12/29/2011 04:47:21 PM:
>
>
> > Hello Jean.
> >
> > Thank you for your prompt response.
> >
> > I have an economic model. I start out with a list of asset levels
> > 1:nks. For every asset level, a person has nis possible income
> > levels and njs possible expense levels, with probabilities pi and
> > pj. They are independent so the probability matrix is just pi%o%pj.
> >
> > For each income level, expense level and starting asset level I will
> > end up with a specific known utility level next time around. That is
> > Vold. I know what Vold[income,expense,new savings level] is. I need
> > a weighted average of these. But the new savings level is itself a
> > function of income, expense and my current saving level.
> >
> > So Vnew[current k] = weighted probability of Vold[each income level,
> > each expense level, new savings given that income and that expense]
> >
> > For simplicity we can say that the ks is i+k-j --
income+savings-expenses.
> >
> > I don't insist on solving this using the technique I asked about,
> > but that was how I went about approaching the issue. I would welcome
> > an alternative algorithm as much as an R fix!
> >
> > Many thanks
> > Asher
>
>
> Thanks. That was helpful. I created a simple function, ksfunction
> () to represent the example you gave. I included two additional
> arguments to deal with newly created k values that extend beyond the
> third dimension of your initial array. Then, I took advantage of
> the fact that you can extract elements from an array using a matrix
> of indices.
>
> Excerpt from ?"[", the help file on extract:
>
> "A third form of indexing is via a numeric matrix with the one
> column for each dimension: each row of the index matrix then selects
> a single element of the array, and the result is a vector. Negative
> indices are not allowed in the index matrix. NA and zero values are
> allowed: rows of an index matrix containing a zero are ignored,
> whereas rows containing an NA produce an NA in the result."
>
> In the code below you can see what I did using a very small example
> for the initial array, Vold. When I increased the size of the array
> to 100^3, the matrix indexing method was 40 times faster than the
> looping method (i.e., it completed in 1/40th the time).
>
> By the way, it's helpful to cc the R-help with all of your replies,
> so that the thread of discussion is maintained for everyone to see.
>
> Hope this helps.
>
> Jean
>
>
> ksfunction <- function(i, j, k, maxk, def=NA) {
> newk <- i - j + k
> newk <- ifelse(newk < 1 | newk > maxk, def, i - j + k)
> newk
> }
>
> nis <- 5
> njs <- 2
> nks <- 5
> tot <- nis*njs*nks
>
> Vold <- array(seq(tot), c(nis, njs, nks))
>
> old.index <- cbind(i=rep(1:nis, njs*nks),
> j=rep(rep(1:njs, rep(nis, njs)), nks),
> k=rep(1:nks, rep(njs*nks, nks)))
> new.index <- cbind(old.index[, 1:2],
> k = ksfunction(old.index[, 1], old.index[, 2], old.index[, 3],
nks))
> Vnew <- array(Vold[new.index], c(nis, njs, nks))
>
>
> > On Fri, Dec 30, 2011 at 12:32 AM, Jean V Adams <jvad...@usgs.gov>
wrote:
> >
> > Asher Meir wrote on 12/29/2011 02:58:00 PM:
> >
> >
> > > I want to create a new array which selects values from an original
array
> > > based on a function of the indices. That is:
> > >
> > > I want to create a new matrix Vnew[i,j,k]=Vold[i,j,ks] where ks is a
> > > function of the index elements i,j,k. I want to do this WITHOUT a
loop.
> > >
> > > Call the function "ksfunction", and the array dimensions
> nis,njs,nks. I can
> > > do this using a loop as follows:
> > >
> > > # Loop version:
> > > Vnew<-array(NA,c(nis,njs,nks)
> > > for(i1 in 1:nis)for(j1 in 1:njs)for(k1 in
> > > 1:nks)Vnew[i1,j1,k1]<-Vold[i1,k1,ksfunction(i1,j1,k1)]
> > >
> > > I already know how to create an array of the ks's:
> > > ksarray[i,j,k]=ksfunction(i,j,k) # I have the array ksarray ready
> > >
> > > I don't want a loop because nis,njs, and nks are pretty large and it
takes
> > > forever.
> > >
> > > Would appreciate help with this issue.
> >
> >
> > There may be non-loopy ways to do this, but in order (for me, at
> > least) to suggest something, it would help to know what ksfunction()
does.
> >
> > Jean
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