This is what I refer to as "ab uno disce omnes"-thinking:
-- from one example all is revealed -- but this is the antithesis
of statistical thinking.
Here is an example function, others can probably do better.
On a vector of length 300 it takes .018 seconds on my aging
G5 ppc mac:
pears <- function(x){
n <- length(x)
A <- combos(n,2)
rbind(x[A[1,]],x[A[2,]])
}
This is actually a (shameless) advertisement for the function
combos in my quantreg package which uses a slightly fancy
algorithm to order the n choose p indices as, say produced
by base R's combn() in such a way that adjacent columns
differ by only one element.
PS. Isn't there a better way to do the indexing on the last line?
url: www.econ.uiuc.edu/~roger Roger Koenker
email [EMAIL PROTECTED] Department of Economics
vox: 217-333-4558 University of Illinois
fax: 217-244-6678 Champaign, IL 61820
On Apr 30, 2008, at 3:15 PM, Zhandong Liu wrote:
I am switching from Matlab to R, but I found that R is 200 times
slower than
matlab.
Since I am newbie to R, I must be missing some important programming
tips.
Please help me out on this.
Here is the function:
## make the full pair-wise permutation of a vector
## input_fc=c(1,2,3);
## output_fc=(
1 1 1 2 2 2 3 3 3
1 2 3 1 2 3 1 2 3
);
grw_permute = function(input_fc){
fc_vector = input_fc
index = 1
k = length(fc_vector)
fc_matrix = matrix(0,2,k^2)
for(i in 1:k){
for(j in 1:k){
fc_matrix[index] = fc_vector[i]
fc_matrix[index+1] = fc_vector[j]
index = index+2
}
}
return(fc_matrix)
}
For an input vector of size 300. It took R 2.17 seconds to run.
But the same code in matlab only needs 0.01 seconds to run.
Am I missing sth in R.. Is there a away to optimize. ???
Thanks
--
Zhandong Liu
Genomics and Computational Biology
University of Pennsylvania
616 BRB II/III, 421 Curie Boulevard
University of Pennsylvania School of Medicine
Philadelphia, PA 19104-6160
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