Michael:
I'm sorry, but this "advice" is wrong. replicate() **IS** essentially
a loop: it uses sapply(), which is basically an interpreted loop (with
suitable caveats that R experts can provide).
The correct advice is: whenever possible, move the loops down to
underlying C code by vectorizing. In the example below, one can partly
do this by removing the random number generation from the loop and
structuring the result as a matrix:
rmat <- matrix(rnorm(2.5e7, nrow=250)) ## each column is a sample of 250
Now one must use apply() -- which is a loop for -- quantile()
out <- apply(rmat,2,quantile, probs = .95)
Unfortunately, in this case, it won't make much difference, as random
number generation is very fast anyway and the looping for quantile()
is the same either way. In fact, both versions took almost the same
time on my computer (replicate() was actually 3 seconds faster --- 48
vs 51 seconds -- perhaps because sapply() is slightly more efficient
than apply() ).
As here (I think), one often cannot vectorize and must loop in
interpreted code, and for this replicate() is fine and yields nice
clean code. But it's still a loop.
Cheers,
Bert
On Fri, Aug 5, 2011 at 9:15 AM, R. Michael Weylandt
<michael.weyla...@gmail.com> wrote:
> This is a textbook of when NOT to use a loop in R: rather make a function
> that does what you want and use the replicate function to do it repeatedly.
>
> f <- function(){
> return(-1000*quantile(rnorm(250,0,0.2),0.95)
> }
>
> x = replicate(1e5,f())
>
> There are your desired numbers.
>
> Some general coding principles: firstly, you don't need to convert to time
> series: quantile immediately undoes that so you've just wasted the time
> doing the coercions each direction. Secondly, the quantiles of c*X are
> exactly c times the quantiles of X so you can cut down on the
> multiplications needed by doing the -1000 multiplication after
> quantilization.
>
> Specific to R: don't use loops unless entirely necessary. (And it's hardly
> ever necessary) -- replicate is great for repeated operations, apply is
> great for looping "over" thins.
>
> More broadly, what do you intend to do with the v95 values? There are
> probably much more efficient ways to get the desired numbers or even closed
> form results. I believe this idea is widely studied as VaR in finance.
>
> Feel free to write back if we can be of more help.
>
> Hope this helps,
>
> Michael Weylandt
>
> On Fri, Aug 5, 2011 at 11:35 AM, UnitRoot <akhussa...@gmail.com> wrote:
>
>> Hi,
>> Can someone help me out to create a (for?) loop for the following
>> procedure:
>>
>> x=rnorm(250,0,0.02)
>> library(timeSeries)
>> x=timeSeries(x)
>> P=1000
>> loss=-P*x
>> loss
>> v95=quantile(loss,0.95)
>> v95
>>
>> I would like to generate 100 000 v95 values.
>> Also, can anybody name a source where I could read up basic programming in
>> R?
>> Thank you.
>>
>> --
>> View this message in context:
>> http://r.789695.n4.nabble.com/Loop-noob-question-tp3721500p3721500.html
>> Sent from the R help mailing list archive at Nabble.com.
>>
>> ______________________________________________
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>>
>
> [[alternative HTML version deleted]]
>
> ______________________________________________
> R-help@r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>
--
"Men by nature long to get on to the ultimate truths, and will often
be impatient with elementary studies or fight shy of them. If it were
possible to reach the ultimate truths without the elementary studies
usually prefixed to them, these would not be preparatory studies but
superfluous diversions."
-- Maimonides (1135-1204)
Bert Gunter
Genentech Nonclinical Biostatistics
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