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## Re: Applying function to vector by index

 From: Paul Kienzle Subject: Re: Applying function to vector by index Date: Mon, 9 Dec 2002 13:53:45 -0500 User-agent: Mutt/1.2.5.1i

```On Mon, Dec 09, 2002 at 01:28:12PM -0500, Paul Kienzle wrote:
> That's because S.^2 implicitly expands S to a full matrix 8-(
> Anyone care to patch it?
>
> Meanwhile, how about the following:
>
> function [count, mean, diff] = spstats(v,j)

Oops!  This should work better:

function [count, mean, var] = spstats(v,j)
i = [1:length(v)];
S = sparse(i,j,v);
count = spsum(sparse(i,j,1));
mean = spsum(S) ./ count;
diff = S - sparse(i,j,mean(j));
var = spsum ( diff .* diff ) ./ (count - 1);
end

>
> Paul Kienzle
>
> On Mon, Dec 09, 2002 at 05:01:47PM -0000, Iago Mosqueira wrote:
> > Thanks, it works fine. I was doing just this for estimating the mean, but I
> > was somehow getting into memory problems (a 65000x1 vector, turning into a
> > sparse matrix with 15 datapoints). It's fine now. Yes, some general way
> > 'sapply' style would be nice...
> >
> > Thanks,
> >
> >
> > iago
> >
> >
> > ----- Original Message -----
> > From: "Paul Kienzle" <address@hidden>
> > To: "Iago Mosqueira" <address@hidden>
> > Sent: 09 December 2002 16:56
> > Subject: Re: Applying function to vector by index
> >
> >
> > > Iago,
> > >
> > > Sparse matrices are a likely candidate here.  Split your data with
> > > one column per bin:
> > >
> > > n = length(d);
> > > S = sparse(1:n,idx,d);
> > >
> > > Then the stats are:
> > >
> > > count = spsum(sparse(1:n,idx,1));
> > >         mean = spsum(S) ./ count;
> > >         var = spsum( (S - sparse(1:n,idx,mean(idx))).^2 ) ./ (count-1);
> > >
> > > Not as good as a generic split/apply operations I agree, but even
> > > with split/apply performance wouldn't be much better than a loop since
> > > you would still be interpreting var(x) for each bin separately.
> > >
> > > Paul Kienzle
> > >
> > > On Mon, Dec 09, 2002 at 04:22:17PM -0000, Iago Mosqueira wrote:
> > > > Hi,
> > > >
> > > > I have a long vector classified in ten bins of different length
> > according to
> > > > another vector. I want to estimate the variance, or any other function,
> > of
> > > > the values from the first vector for each of the categories described by
> > the
> > > > second, without using a loop. Is there any obvious way of doing this I
> > am
> > > > overlooking? In R one could do
> > > >
> > > >     sapply(split(d,idx),var)
> > > >
> > > > where d is my data vector and idx the index vector.
> > > >
> > > > Many thanks,
> > > >
> > > >
> > > > iago
> > > >
> > > >
> > > >
> > > >
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