R help - May 2005 - parsing speed

Hi everyone,

I have a question on parsing speed.

I have two functions:

F1
F2

As things are now, F2 calls F1 internally:

F2 =  function(x){
if (something == 1){
y = F1(x)
}
if (something ==2){
do whatever
}
}

*Assuming there could be some difference*, is is faster to use the code
as written above or should I actually write the statements of F1 to make
the parsing faster? 

Regards,

Federico Calboli

-- 
Federico C. F. Calboli
Department of Epidemiology and Public Health
Imperial College, St Mary's Campus
Norfolk Place, London W2 1PG

Tel  +44 (0)20 7594 1602     Fax (+44) 020 7594 3193

f.calboli [.a.t] imperial.ac.uk
f.calboli [.a.t] gmail.com

Federico Calboli wrote:
> Hi everyone,
> 
> I have a question on parsing speed.
> 
> I have two functions:
> 
> F1
> F2
> 
> As things are now, F2 calls F1 internally:
> 
> F2 =  function(x){
> if (something == 1){
> y = F1(x)
> }
> if (something ==2){
> do whatever
> }
> }
> 
> *Assuming there could be some difference*, is is faster to use the code
> as written above or should I actually write the statements of F1 to make
> the parsing faster? 
The parsing only happens once when you define the functions, and is 
(almost always) a negligible part of total execution time.  I think 
you're really worried about execution time.  You'll probably get more 
execution time with a separate function because function calls take time.

However, my guess is that putting F1 inline won't make enough difference 
to notice.

Duncan

> From: Barry Rowlingson
> 
> Jan T. Kim wrote:
> 
> > Generally, I fully agree -- modular coding is good, not only in R.
> > However, with regard to execution time, modularisation that involves
> > passing of large amounts of data (100 x 1000 data frames etc.) can
> > cause problems.
> 
>   I've just tried a few simple examples of throwing biggish 
> (3000x3000) 
> matrices around and haven't encountered any pathological 
> behaviour yet. 
> I tried modifying the matrices within the functions, tried 
> looping a few 
> thousand times to estimate the matrix passing overhead, and in most 
> cases the modular version run pretty much as fast as - or 
> occasionally 
> faster than - the inline version. There was some variability 
> in CPU time 
> taken, probably due to garbage collection.
> 
>   Does anyone have a simple example where passing large data 
> sets causes 
> a huge increase in CPU time? I think R is pretty smart with its 
> parameter passing these days - anyone who thinks its still like Splus 
> version 2.3 should update their brains to the 21st Century.
I think one example of this is using the formula interface to fit models on
large data sets, especially those with tons of variables.  Some model
fitting functions have the default interface f(x, y, ...), along with a
formula method f(formula, data, ...).  If x has lots of variables (say over
1000), using the formula interface can take several times longer than
calling the "raw" interface directly.

Andy
 
> Baz
> 
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