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

[Adaikalavan Ramasamy]

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