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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