Hi David,
sorry, I was not clear. 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 )
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 <dwinsem...@comcast.net> 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))
>>> }
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