[R] Applying functions to partitions

[Martin Morgan]

Stavros Macrakis <macrakis at alum.mit.edu> writes:

> Assuming your matrix is:
>
>     mm <- matrix(runif(6*6),6,6)
>
> And your blocks are defined by integers or factors:
>
>    cfact <- c(1,1,1,2,3,3)
>    rfact <- c(1,1,1,2,2,3)
>
> Then the following should do the trick:
>
>    matrix(tapply(mm, outer(rfact,cfact,paste), mean),
>               length(unique(rfact)))

or the variant

  idx <- outer(rfact, (cfact - 1) * max(rfact), "+")
  matrix(tapply(m, idx, mean), max(rfact))

The assumption is that cfact, rfact are integer valued with max(rfact)
<= nrow(m), max(cfact) <= ncol(m).

I think Stavros' solution will run in to trouble when there are more
than 9 row blocks, and '10 1' sorts before '2 1', for instance.

Martin


> The 'outer' calculates a joint factor for each element of the matrix; the
> 'tapply' treats the matrix as a vector, grouping by factor and calculating
> means; the 'matrix' rearranges them as a matrix corresponding to the
> original block structure.
>
> Is that what you had in mind?
>
>               -s
>
>
> On Mon, Feb 16, 2009 at 12:43 PM, Titus von der Malsburg <malsburg at gmail.com
>> wrote:
>
>>
>> Hi list!  I have a large matrix which I'd like to partition into blocks
>> and for each block I'd like to compute the mean.  Following a example
>> where each letter marks a block of the partition:
>>
>>     a a a d g g
>>     a a a d g g
>>     a a a d g g
>>     b b b e h h
>>     b b b e h h
>>     c c c f i i
>>
>> I'm only interested in the resulting matrix of means.  How can this be
>> done efficiently?
>>
>> Thanks!  Titus
>>
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>>
>
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>
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-- 
Martin Morgan
Computational Biology / Fred Hutchinson Cancer Research Center
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