Hi,
I'm trying to create a distance matrix. And it works out somewhat ok. However,
I suspect that there are
some efficiency issues with my efforts. Plz have a look at this:
donor <- matrix(c(3,1,2,3,3,1,4,3,5,1,3,2), ncol=4)
receiver <-
matrix(c(1,4,3,2,4,3,1,5,1,3,2,1,4,5,3,5,1,3,2,4,5,1,2,3,1,4,5,5,1,2,1,3,4,3,2,5,5,1,4,2,5,4,3,2),
ncol=4)
The above creates my two matrices. I have three donors, and eleven receivers
(rows), with four
measures (columns) in each matrix.
And now, I want to apply the daisy() function from the cluster library, to
calculate distances between my
three donors, and eleven receivers. The end result should be a 11x3 matrix with
distances between the
units from the two matrices. I can calculate one distance measure (ie donor 1
and receiver 1). Like this:
library(cluster)
daisy(rbind(donor[1,], receiver[1,]), metric = 'gower')
My first attempt was a simple nested for-loop. But that one was discarded after
reading up on efficiency
issues with for-looping. So I turned to 'apply' with this result:
apply(donor, 1, function(b) apply(receiver, 1, function(a) daisy(rbind(b, a),
metric = 'gower')))
[,1] [,2] [,3]
[1,] 1.00 0.50 0.75
[2,] 1.00 0.75 0.75
[3,] 0.75 1.00 1.00
[4,] 0.50 0.75 0.75
[5,] 0.75 1.00 0.75
[6,] 0.75 1.00 0.50
[7,] 0.75 0.50 0.75
[8,] 1.00 1.00 1.00
[9,] 1.00 0.75 1.00
[10,] 0.75 0.50 1.00
[11,] 0.75 1.00 0.25
However, something tells me that there is a simpler (more efficient) way of
doing this. I've been reading
up on the Matrix library, but I'm having trouble understanding the functions...
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