[R] [fixed] vectorized nested loop: apply a function that takes two rows

From: Jose Quesada <quesada_at_gmail.com>
Date: Tue 23 Jan 2007 - 20:46:27 GMT

(Extremely sorry, disregard previous email as I hit send before pasting the latest version of the example; this one is smaller too) Dear R users,

I want to apply a function that takes two vectors as input to all pairs (combinations (nrow(X), 2))of matrix rows in a matrix. I know that ideally, one should avoid loops in R, but after reading the docs for do.call, apply, etc, I still don't know how to write the nested loop in a vectorized way.

Example data:
x = matrix(rnorm(100), 10, 10)
# this is actually a very large sparse matrix, but it doesn't matter for the
# example
x = as(x,"CsparseMatrix")

# cosine function

cosine = function (x, y){

	if (is.vector(x) && is.vector(y)) {
		return(crossprod(x, y)/sqrt(crossprod(x) * crossprod(y)))
	} else {stop("cosine: argument mismatch. Two vectors needed as input.")}

# The loop-based solution I have is:

		if (is(x, "Matrix") ) {
			cos 	= array(NA, c(ncol(x), ncol(x)))
			for (i in 2:ncol(x)) {
				for (j in 1:(i - 1)) {
					cos[i, j] = cosine(x[, i], x[, j])

This solution seems inneficient. Is there an easy way of achieving this with a clever do.call + apply combination?

Also, I have noticed that getting a row from a Matrix object produces a normal array (i.e., it does not inherit Matrix class). However, selecting >1 rows, does produce a same-class matrix. If I convert with as() the output of selecting one row, am I losing performance? Is there any way to make the resulting vector be a 1-D Matrix object?
This solution seems inneficient. Is there an easy way of achieving this with a clever do.call + apply combination?

Thanks in advance,

Jose Quesada, PhD
Research fellow, Psychology Dept.
Sussex University, Brighton, UK

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Received on Wed Jan 24 07:57:18 2007

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