set.seed "sets" the (pseudo-)random number generator in a predictable
state so that you get the same table as I do when running the code,
assuming you don't do any other calls to the RNG in the interim.
123 is kind of traditional as a seed for demonstration purposes, but
in real analyses you could choose any seed favorite number.
--
David.
On Feb 7, 2011, at 9:51 AM, B77S wrote:
Also, I really appreciate you explaining why you used factor. I'm
still not
quite sure what set.seed does (i read ?set.seed) or why you chose
123... but
it and the function below work, so that is all that matters. :)
randSub <- function(L1, s.size)
{
set.seed(123)
samptbl <- apply(L1, 1, function(x) sample(colnames(L1), s.size,
prob=x,
replace=TRUE) )
sampdf <- as.data.frame(samptbl)
sampdf1 <-vector("list")
for(i in 1:nrow(L1))
{
sampdf1[[i]] <- factor(sampdf[[i]], levels= colnames(L1))
}
out <- t(sapply(sampdf1, table))
}
--
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David Winsemius, MD
West Hartford, CT
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