From: Gavin Simpson <gavin.simpson_at_ucl.ac.uk>

Date: Tue, 22 Apr 2008 18:26:58 +0100

Date: Tue, 22 Apr 2008 18:26:58 +0100

On Tue, 2008-04-22 at 12:59 -0400, stephen sefick wrote:

> d = c(0L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0L, 0L, 7375L,

*> NA, NA, 17092L, 0L, 0L, 32390L, 2326L, 22672L, 13550L, 18285L)
**>
**> boot.out <-boot(d, mean, R=1000, sim="permutation")
**>
**> Error in mean.default(data, original, ...) :
**> 'trim' must be numeric of length one
**>
**> I know that I am missing something but I can't figure it out.
*

You aren't reading the documentation closely enough. ?boot informs us that for all sim other than "parametric", 'statistic' must be a function that takes two arguments. The second argument to mean.default is trim, and boot is passing to mean a vector of indices as argument 'trim' which it is not expecting and quite rightly throws a wobbly.

Write a wrapper to mean, that accepts two arguments, one the data vector and one the permuted indices, then use these to form a call to mean --- here mean.fun does this (note we turn on removing NA's by default otherwise it wouldn't work)

*> mean.fun <- function(dat, idx) mean(dat[idx], na.rm = TRUE)
*

> boot.out <- boot(d, mean.fun, R=1000, sim="ordinary")

> boot.out

**ORDINARY NONPARAMETRIC BOOTSTRAP
**
Call:

boot(data = d, statistic = mean.fun, R = 1000, sim = "ordinary")

Bootstrap Statistics :

original bias std. error

t1* 9474.167 -3.373422 3110.968

There doesn't seem to be much point in permuting the data here, the mean will be the same, regardless of what permutation you take. The above does an ordinary bootstrap instead.

**HTH
**
G

*> thanks
**>
*

> stephen

*>
*

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