On Aug 26, 2012, at 5:06 PM, Jinsong Zhao wrote:
Hi there,
In my code, there is a for loop like the following:
pmatrix <- matrix(NA, nrow = 99, ncol = 10000)
qmatrix <- matrix(NA, nrow = 99, ncol = 3)
paf <- seq(0.01, 0.99, 0.01)
for (i in 1:10000) {
p.r.1 <- rnorm(1000, 1, 0.5)
p.r.2 <- rnorm(1000, 2, 1.5)
p.r.3 <- rnorm(1000, 3, 1)
pmatrix[,i] <- quantile(c(p.r.1, p.r.2, p.r.3), paf)
}
for (i in 1:99) {
qmatrix[i,] <- quantile(pmatrix[i,], c(0.05, 0.5, 0.95))
}
Because of the number of loop is very large, e.g., 10000 here, the
code is very slow.
I would think that picking the seq(0.01, 0.99, 0.01) items in the
first case and the 500th, 5000th and the 9500th in the second case,
rather than asking for what `quantile` would calculate, would surely
be more "statistical", in the sense of choose order statistics anyway.
Likely much faster.
--
David.
Is it possible to optimize the code?
Any suggestion will be greatly appreciated.
Regards,
Jinsong
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David Winsemius, MD
Alameda, CA, USA
______________________________________________
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and provide commented, minimal, self-contained, reproducible code.
