On Nov 20, 2014, at 2:23 AM, i.petzev wrote:
> Hi David,
>
> sorry, I was not clear.
Right. You never were clear about what you wanted and your examples was so
statistically symmetric that it is still hard to see what is needed. The
examples below show CI's that are arguably equivalent. I can be faulted for
attempting to provide code that produced a sensible answer to a vague question
to which I was only guessing at the intent.
> The difference comes from defining or not defining “w” in the boot()
> function. The results with your function and your approach are thus:
>
> set.seed(1111)
> x <- rnorm(50)
> y <- rnorm(50)
> weights <- runif(50)
> weights <- weights / sum(weights)
> dataset <- cbind(x,y,weights)
>
> vw_m_diff <- function(dataset,w) {
> differences <- dataset[w,1]-dataset[w,2]
> weights <- dataset[w, "weights"]
> return(weighted.mean(x=differences, w=weights))
> }
> res_boot <- boot(dataset, statistic=vw_m_diff, R = 1000, w=dataset[,3])
> boot.ci(res_boot)
>
> BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS
> Based on 1000 bootstrap replicates
>
> CALL :
> boot.ci(boot.out = res_boot)
>
> Intervals :
> Level Normal Basic
> 95% (-0.5657, 0.4962 ) (-0.5713, 0.5062 )
>
> Level Percentile BCa
> 95% (-0.6527, 0.4249 ) (-0.5579, 0.5023 )
> Calculations and Intervals on Original Scale
>
> ********************************************************************************************************************
>
> However, without defining “w” in the bootstrap function, i.e., running an
> ordinary and not a weighted bootstrap, the results are:
>
> res_boot <- boot(dataset, statistic=vw_m_diff, R = 1000)
> boot.ci(res_boot)
>
> BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS
> Based on 1000 bootstrap replicates
>
> CALL :
> boot.ci(boot.out = res_boot)
>
> Intervals :
> Level Normal Basic
> 95% (-0.6265, 0.4966 ) (-0.6125, 0.5249 )
I hope you are not saying that because those CI's are different that there is
some meaning in that difference. Bootstrap runs will always be "different" than
each other unless you use set.seed(.) before the runs.
>
> Level Percentile BCa
> 95% (-0.6714, 0.4661 ) (-0.6747, 0.4559 )
> Calculations and Intervals on Original Scale
>
> On 19 Nov 2014, at 17:49, David Winsemius <[email protected]> wrote:
>
>>>> vw_m_diff <- function(dataset,w) {
>>>> differences <- dataset[w,1]-dataset[w,2]
>>>> weights <- dataset[w, "weights"]
>>>> return(weighted.mean(x=differences, w=weights))
>>>> }
>
David Winsemius
Alameda, CA, USA
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