In your second example the boot function will still generate its
random indicies before your internal function calls sample, so the
seed will be different when you call sample from what you originally
set it to. If you really want to know what the boot function does,
look at its code (it does some things to try and be more efficient
which might give different results than a simpler version).
On Wed, Feb 8, 2012 at 1:45 PM, Deng Nina wrote:
> Hi,there,
>
> I am using R package "boot" to bootstrap. I have one question here: does
> anybody possibly know how the boot package generates the "indices" which is
> used in the statistic function?
>
> I thought "indices = sample(data, replace=TRUE)", but when I replaced
> "indices" with this command and used "boot", I got different results.
>
> Specifically, below are the codes for illustration.
>
> (1) The typical way by generating indices in the package:
> boot1 <- function (data, indices)
> {
> d <- data[indices]
> return(d)
> }
> AA <- c(1:10)
> require(boot)
> set.seed(123)
> results1 <- boot(data= AA, statistic=boot1, R=100)
>
> (2) The alternative way by calculating "indices" myself:
> boot2 <- function (data,indices)
> {
> indices <- sample(data, replace=TRUE)
> d <- data[indices]
> return(d)
> }
> AA <- c(1:10)
> set.seed(123)
> results2 <- boot(data= AA, statistic=boot2, R=100)
>
> When I looked up using results1$t and results2$t, I had totoally different
> bootstrap samples. I found this even had great impacts on the results in my
> study. Does the second approach have any problem? Anyone could provide any
> inputs on this? Thank you very much in advance!
> Regards
> Nina
>
> [[alternative HTML version deleted]]
>
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--
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