The following version of boot:::ordinary.array will enable this to run in
300Mb:
ordinary.array <- function(n, R, strata)
{
inds <- as.integer(names(table(strata)))
if (length(inds) == 1) {
output <- sample(n, n*R, replace=TRUE)
dim(output) <- c(R, n)
} else {
I was suggesting adding the gc() call to help provide some additional
information on the utilization of memory. As you indicated, it
probably do not help in reducing the fragmentation of memory, but it
was worth a try to see if there was any additional information that
might be gleaned from the ex
On Sat, 2 Aug 2008, Tom La Bone wrote:
I have distilled my bootstrap problem down to this bit of code, which
calculates an estimate of the 95th percentile of 7500 random numbers drawn
from a standard normal distribution:
library(boot)
per95 <- function( annual.data, b.index) {
sample.data <- a
I have distilled my bootstrap problem down to this bit of code, which
calculates an estimate of the 95th percentile of 7500 random numbers drawn
from a standard normal distribution:
library(boot)
per95 <- function( annual.data, b.index) {
sample.data <- annual.data[b.index]
return(quantile(s
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