Calculate The Bootstrap Standard Error Of The 75th Percentile
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The R package boot allows a user to easily generate bootstrap samples of virtually any statistic that they can calculate in bootstrap percentile confidence interval R. From these samples, you can generate estimates of bias, bootstrap confidence
Percentile Method Confidence Intervals
intervals, or plots of your bootstrap replicates. We will demonstrate a few of these techniques in this page and
Bootstrap Confidence Interval Example
you can read more details at its CRAN package page. Before using commands in the boot package, you must first download the package and load it in your workspace. We
Bootstrap Percentile Confidence Interval In R
will be using the hsb2 dataset for all of the examples on this page. install.packages("boot") library(boot) hsb2<-read.table("http://www.ats.ucla.edu/stat/data/hsb2.csv", sep=",", header=T) Using the boot commandThe boot command executes the resampling of your dataset and calculation of your statistic(s) of interest on these samples. Before calling boot, you need to define a function that will return the statistic(s) that you would like to bootstrap. The the bootstrap method of constructing confidence intervals can be used to estimate first argument passed to the function should be your dataset. The second argument can be an index vector of the observations in your dataset to use or a frequency or weight vector that informs the sampling probabilities. The example below uses the default index vector and assumes we wish to use all of our observations. The statistic of interest here is the correlation coefficient of write and math. f <- function(d, i){ d2 <- d[i,] return(cor(d2$write, d2$math)) }With the function fc defined, we can use the boot command, providing our dataset name, our function, and the number of bootstrap samples to be drawn.bootcorr ORDINARY NONPARAMETRIC BOOTSTRAP Call: boot(data = hsb2, statistic = c, R = 500) Bootstrap Statistics : original bias std. error t1* 0.6174493 -0.004455323 0.04169738While the printed output for bootcorr is brief, R saves additional information that can be listed:summary(bootcorr) Length Class Mode t0 1 -none- numeric t 500 -none- numeric R 1 -none- numeric data 11 data.frame list seed 626 -none- numeric statistic 1 -none- function sim 1 -none- character call 4 -none- call stype 1 -none- character st
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