[R] dist like function but where you can configure the method

[Rui Barradas]

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 <wewolski at 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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