Yes, ... and further
apply-type functions still have to loop at the interpreter level, and
generally take about the same time as their translation to for loops
(with suitable caveats for this kind of vague assertion). Their chief
advantage is readability and adherence to R's functional paradigm
(again with suitable caveats).
Alternatively, byte code compilation with the compiler package **may**
(significantly) improve speed, but it very much depends ...
Cheers,
Bert
Bert Gunter
Genentech Nonclinical Biostatistics
(650) 467-7374
"Data is not information. Information is not knowledge. And knowledge
is certainly not wisdom."
H. Gilbert Welch
On Fri, May 16, 2014 at 9:12 AM, Barry Rowlingson
<b.rowling...@lancaster.ac.uk> wrote:
> 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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