Calling gc() before starting a memory-intensive task is normally a good
idea, as it helps avoid memory fragmentation (which is possibly a problem
in a 32-bit OS, but you did not say). R 2.1.0 beta has some dodges to
help, so you may find if helpful to try that out.
On Mon, 4 Apr 2005, Mike Hickerson wrote:
Hello
I am getting memory allocation errors when running a function that uses
locfit within a for loop. After 25 or so loops, it gives this error.
"Error: cannot allocate vector of size 281250 Kb"
Running on linux cluster with a Gb of RAM. Problem never happens on my
OS X (less memory). The total data is 130 cols by 5000 rows
The first 129 cols are response variables, the 130th is the parameter
The function fits a local regression between the 129 variables in the
ith row of m[ ] to the 129 variables in 5000 rows after m was fed into
130 different vectors called Var1, .....Var129, and PARAMETER.
array <- scan(("DataFile"),nlines=5000)
m<-matrix(array,ncol=130,byrow=T)
for (i in 1:200)
{
result<-
function(m[i,c(1,....,129)],PARAMETER,cbind(Var1,...,Var129)seq(1,len=50
00),F)
}
Any ideas on how to avoid this memory allocation problem would be
greatly appreciated. Garbage collection? (or is that too slow?)
Many Thanks in Advance!
Mike
Mike Hickerson
University of California
Museum of Vertebrate Zoology
3101 Valley Life Sciences Building
Berkeley, California� 94720-3160� USA
voice 510-642-8911
cell: 510-701-0861
fax 510-643-8238
[EMAIL PROTECTED]
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