From help-octave-request at bevo dot che dot wisc dot edu Mon Dec  9 10:56:54 2002
Subject: Re: Applying function to vector by index
From: Paul Kienzle <pkienzle at jazz dot ncnr dot nist dot gov>
To: Iago Mosqueira <i dot mosqueira at ic dot ac dot uk>
Cc: octave-help <help-octave at bevo dot che dot wisc dot edu>
Date: Mon, 9 Dec 2002 11:56:46 -0500

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
pkienzle at users dot sf dot net

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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