I actually have to refine my pseudocode. 'result' is a numerical vector of length 7, and is binded with whole results through an rbind() :
for (k in replicates) {
data <- sampling from a population
for (i in param1) {
for (j in param2) {
result <- function(i, j, data)
all.results <- rbind(all.results, result)
}
}
}all.result is at most a 220 rows and 7 columns data frame, which doesn't seem to be big enough to explain such a slow computation.
Moreover, previous computations with a sample size of 100, which took individually about 4 seconds at most, ran effectively in a little bit more than 15 minutes for the whole set.
The problem arise with a sample size of 500, increasing single function computation time normally, but not the whole process !?
At 11:37 28/05/03, you wrote:
Yves Brostaux <[EMAIL PROTECTED]> writes:
> 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 ?
The problem with pseudocode: You didn't really overwrite the "result" every time did you? I bet you stored it somewhere.
Two common causes of inefficiency are (a) that the stored objects may be large and (b) some naive ways of storing the results involve copying all preceding results, e.g.
list.of.results <- list() for (.....){ result <- ... list.of.results <- c(list.of.results, result) }
The fix for (a) is to extract what you need and discard the rest and for (b) to allocate the list up front with the proper length and assign to list.of.results[[i]].
-- O__ ---- Peter Dalgaard Blegdamsvej 3 c/ /'_ --- Dept. of Biostatistics 2200 Cph. N (*) \(*) -- University of Copenhagen Denmark Ph: (+45) 35327918 ~~~~~~~~~~ - ([EMAIL PROTECTED]) FAX: (+45) 35327907
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