Hi,
It's kind of weird for me to not see a return() statement in the
function. Maybe try rolling your own, nonparametric bootstraps aren't all
that bad.
i.e.
reps=1000
stat.holder=rep(NA,reps) ###Rep of NA's so you can pick up errors easily
for(count in 1:reps)
{
bootsample=sample(data, n.data.to.compute.from,replace=T)
##Insert code here to compute statistic
stat.holder[count]=computed.statistic
}
It may be overtly simplistic, but you may find it helpful.
Thanks,
Ken
On Fri, Aug 12, 2011 at 10:10 AM, Alex Olssen <alex.ols...@gmail.com> wrote:
> Hi R-help,
>
> I am trying to implement a nonparametric bootstrap to find the
> standard errors of a simple statistics - the ratio of two scalars. I
> am having difficulty getting boot() to work correctly. I code a
> function to create the ratio of the relevant scalars. theta(data, i).
> When I call the function for my data and every observation appearing
> once, theta(test, c(1)), I get the correct statistic for my original
> data. However when I use boot(test, theta, 200) the original
> statistic is incorrect.
>
> My code and data are below. The code is very short.
>
> Any help will be appreciated.
>
> Cheers,
> Alex
>
> library(boot)
> test <- read.csv("test.csv")
> test.mean <- mean(test)
> test.cov <- cov(test)*87/88 ## use the biased version as I am
> reproducing a result from a book
> test.eigen <- eigen(test.cov)
> theta <- function(data, i) {
> data <- data * i
> data.cov <- cov(data)
> data.eigen <- eigen(data.cov)
> data.eigen$values[1]/sum(data.eigen$values)
> }
> test.boot <- boot(test, theta, 200)
>
> test
> mec vec alg ana sta
> 1 77 82 67 67 81
> 2 63 78 80 70 81
> 3 75 73 71 66 81
> 4 55 72 63 70 68
> 5 63 63 65 70 63
> 6 53 61 72 64 73
> 7 51 67 65 65 68
> 8 59 70 68 62 56
> 9 62 60 58 62 70
> 10 64 72 60 62 45
> 11 52 64 60 63 54
> 12 55 67 59 62 44
> 13 50 50 64 55 63
> 14 65 63 58 56 37
> 15 31 55 60 57 73
> 16 60 64 56 54 40
> 17 44 69 53 53 53
> 18 42 69 61 55 45
> 19 62 46 61 57 45
> 20 31 49 62 63 62
> 21 44 61 52 62 46
> 22 49 41 61 49 64
> 23 12 58 61 63 67
> 24 49 53 49 62 47
> 25 54 49 56 47 53
> 26 54 53 46 59 44
> 27 44 56 55 61 36
> 28 18 44 50 57 81
> 29 46 52 65 50 35
> 30 32 45 49 57 64
> 31 30 69 50 52 45
> 32 46 49 53 59 37
> 33 40 27 54 61 61
> 34 31 42 48 54 68
> 35 36 59 51 45 51
> 36 56 40 56 54 35
> 37 46 56 57 49 32
> 38 45 42 55 56 40
> 39 42 60 54 49 33
> 40 40 63 53 54 25
> 41 23 55 59 53 44
> 42 48 48 49 51 37
> 43 41 63 49 46 34
> 44 46 52 53 41 40
> 45 46 61 46 38 41
> 46 40 57 51 52 31
> 47 49 49 45 48 39
> 48 22 58 53 56 41
> 49 35 60 47 54 33
> 50 48 56 49 42 32
> 51 31 57 50 54 34
> 52 17 53 57 43 51
> 53 49 57 47 39 26
> 54 59 50 47 15 46
> 55 37 56 49 28 45
> 56 40 43 48 21 61
> 57 35 35 41 51 50
> 58 38 44 54 47 24
> 59 43 43 38 34 49
> 60 39 46 46 32 43
> 61 62 44 36 22 42
> 62 48 38 41 44 33
> 63 34 42 50 47 29
> 64 18 51 40 56 30
> 65 35 36 46 48 29
> 66 59 53 37 22 19
> 67 41 41 43 30 33
> 68 31 52 37 27 40
> 69 17 51 52 35 31
> 70 34 30 50 47 36
> 71 46 40 47 29 17
> 72 10 46 36 47 39
> 73 46 37 45 15 30
> 74 30 34 43 46 18
> 75 13 51 50 25 31
> 76 49 50 38 23 9
> 77 18 32 31 45 40
> 78 8 42 48 26 40
> 79 23 38 36 48 15
> 80 30 24 43 33 25
> 81 3 9 51 47 40
> 82 7 51 43 17 22
> 83 15 40 43 23 18
> 84 15 38 39 28 17
> 85 5 30 44 36 18
> 86 12 30 32 35 21
> 87 5 26 15 20 20
> 88 0 40 21 9 14
>
> ______________________________________________
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>
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