Slow computation in for loop

I don't know if this will help you or not, but might worth a try.  You can
replace the two inner for loops with nested calls to sapply().  For example:
sapply(1:5, function(x) sapply(6:10, function(y) x+y))
[,1] [,2] [,3] [,4] [,5]
[1,]    7    8    9   10   11
[2,]    8    9   10   11   12
[3,]    9   10   11   12   13
[4,]   10   11   12   13   14
[5,]   11   12   13   14   15

Using sapply() this way is a sneaky way of avoiding explicit for loops, but
whether it actually saves resources, you have to try and see.

HTH,
Andy
-----Original Message-----
From: Yves Brostaux [mailto:brostaux.y at fsagx.ac.be]
Sent: Wednesday, May 28, 2003 4:30 AM
To: r-help at stat.math.ethz.ch
Subject: [R] Slow computation in for loop

Dear members,

I'm using R to do some test computation on a set of parameters of a 
function. This function is included in three for() loops, 
first one for 
replications, and the remaining two cycling through possible 
parameters 
values, like this :

for (k in replicates) {
   data <- sampling from a population
   for (i in param1) {
     for (j in param2) {
        result <- function(i, j, data)
     }
   }
}

With the 'hardest' set of parameters, a single computation of 
the function 
take about 16s on an old Sun Sparc workstation with 64 Mb RAM 
and don't 
access a single time to disk.

But when I launch the for() loops (which generate 220 
function calls), disk 
gets very sollicitated and the whole process takes as much as 8 to 10 
hours, instead of the expected 1 hour.

What's wrong here ? Is there a thing I don't know about for() 
loops, and a 
way to correct it ?

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