one approach is the following:
mat <- rbind(c(-1, -1, 2, NA), c(3, 3, -2, -1), c(1, 1, NA, -2))
mat / ave(abs(mat), row(mat), sign(mat), FUN = sum)
I hope it helps.
Best,
Dimitris
Hao Cen wrote:
Hi,
I have a matrix with positive numbers, negative numbers, and NAs. An
example of the matrix is as follows
-1 -1 2 NA
3 3 -2 -1
1 1 NA -2
I need to compute a scaled version of this matrix. The scaling method is
dividing each positive numbers in each row by the sum of positive numbers
in that row and dividing each negative numbers in each row by the sum of
absolute value of negative numbers in that row.
So the resulting matrix would be
-1/2 -1/2 2/2 NA
3/6 3/6 -2/3 -1/3
1/2 1/2 NA -2/2
Is there an efficient way to do that in R? One way I am using is
1. rowSums for positive numbers in the matrix
2. rowSums for negative numbers in the matrix
3. sweep(mat, 1, posSumVec, posDivFun)
4. sweep(mat, 1, negSumVec, negDivFun)
posDivFun = function(x,y) {
xPosId = x>0 & !is.na(x)
x[xPosId] = x[xPosId]/y[xPosId]
return(x)
}
negDivFun = function(x,y) {
xNegId = x<0 & !is.na(x)
x[xNegId] = -x[xNegId]/y[xNegId]
return(x)
}
It is not fast enough though. This scaling is to be applied to large data
sets repetitively. I would like to make it as fast as possible. Any
thoughts on improving it would be appreciated.
Thanks
Jeff
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--
Dimitris Rizopoulos
Assistant Professor
Department of Biostatistics
Erasmus University Medical Center
Address: PO Box 2040, 3000 CA Rotterdam, the Netherlands
Tel: +31/(0)10/7043478
Fax: +31/(0)10/7043014
______________________________________________
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
