Hello,
The compiler package is good at speeding up for loops but in this case
the gain is neglectable. The ks test is the real time problem.
library(compiler)
f1 <- function(n){
for(i in 1:100){
for(i in 1:100){
ks.test(runif(100),runif(100))
}
}
}
f1.c <- cmpfun(f1)
system.time(f1())
user system elapsed
3.50 0.00 3.53
system.time(f1.c())
user system elapsed
3.47 0.00 3.48
Rui Barradas
Em 16-05-2014 17:12, Barry Rowlingson escreveu:
On Fri, May 16, 2014 at 4:46 PM, Witold E Wolski <wewol...@gmail.com> wrote:
Dear Jari,
Thanks for your reply...
The overhead would be
2 for loops
for(i in 1:dim(x)[2])
for(j in i:dim(x)[2])
isn't it? Or are you seeing a different way to implement it?
A for loop is pretty expensive in R. Therefore I am looking for an
implementation similar to apply or lapply were the iteration is made
in native code.
No, a for loop is not pretty expensive in R -- at least not compared
to doing a k-s test:
> system.time(for(i in 1:10000){ks.test(runif(100),runif(100))})
user system elapsed
3.680 0.012 3.697
3.68 seconds to do 10000 ks tests (and generate 200 runifs)
> system.time(for(i in 1:10000){})
user system elapsed
0.000 0.000 0.001
0.000s time to do 10000 loops. Oh lets nest it for fun:
> system.time(for(i in 1:100){for(i in
1:100){ks.test(runif(100),runif(100))}})
user system elapsed
3.692 0.004 3.701
no different. Even a ks-test with only 5 items is taking me 2.2 seconds.
Moral: don't worry about the for loops.
Barry
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