[R] howto optimize operations between pairs of rows in a single matrix like cor and pairs

[Adaikalavan Ramasamy]

Thank you to Jim and Moshe. I will try the Rprof option as well as the 
running the function to run on columns instead. Thank you.



jim holtman wrote:
> Use Rprof to see where time is being spent.  If it is in FUN, then
> there is probably no way to "optimize" outside of changing the way FUN
> works.  So the first thing is to decide where time is being spent.
> 
> On Sun, Aug 24, 2008 at 6:35 PM, Adaikalavan Ramasamy
> <a.ramasamy at imperial.ac.uk> wrote:
>> Hi,
>>
>> I calculating the output of a function when applied to pairs of row from a
>> single matrix or dataframe similar to how cor() and pairs() work. This is
>> the code that I have been using:
>>
>>   pairwise.apply <- function(x, FUN, ...){
>>
>>
>>     n <- nrow(x)
>>     r <- rownames(x)
>>     output <- matrix(NA, nc=n, nr=n, dimnames=list(r, r))
>>
>>
>>     for(i in 1:n){
>>       for(j in 1:n){
>>         if(i >= j) next()
>>         output[i, j] <- FUN( x[i,], x[j,] )
>>       }
>>     }
>>     return(output)
>>   }
>>
>> I realize that the output of the pairwise operation needs to be scalar. Here
>> is an example. The actual function and dataset I want to use is more
>> complicated and thus the function runs slow for large datasets.
>>
>>   m <- iris[ 1:5, 1:4 ]
>>
>>   pairwise.apply(m, sum)
>>      1    2    3    4    5
>>   1 NA 19.7 19.6 19.6 20.4
>>   2 NA   NA 18.9 18.9 19.7
>>   3 NA   NA   NA 18.8 19.6
>>   4 NA   NA   NA   NA 19.6
>>   5 NA   NA   NA   NA   NA
>>
>> Can I use apply() or any of it's family to optimize the codes? I have tried
>> playing around with outer, kronecker, mapply without any sucess.
>>
>> Any suggestions? Thank you.
>>
>> Regards, Adai
>>
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>>
> 
> 
>