Re: [R] bootstrap confidence intervals with previously existing bootstrap sample
On Tue, 4 Sep 2007, [EMAIL PROTECTED] wrote: I am new to R. I would like to calculate bootstrap confidence intervals using the BCa method for a parameter of interest. My situation is this: I already have a set of 1000 bootstrap replicates created from my original data set. I have already calculated the statistic of interest for each bootstrap replicate, and have also calculated the mean for this statistic across all the replicates. Now I would like to calculate Bca confidence intervals for this statistic. Is there a way to import my previously-calculated set of 1000 statistics into R, and then calculate bootstrap confidence intervals around the mean from this imported data? I have found the code for boot.ci in the manual for the boot package, but it looks like it requires that I first use the boot function, and then apply the output to boot.ci. Because my bootstrap samples already exist, I don't want to use boot, but just want to import the 1000 values I have already calculated, and then get R to calculate the mean and Bca confidence intervals based on these values. Is this possible? Brian Ripley wrote: Yes, it is possible but you will have to study the internal structure of an object of class boot (which is documented on the help page) and mimic it. You haven't told us which type of bootstrap you used, which is one of the details you need to supply. It might be slightly easier to work with function bcanon in package bootstrap, which you would need to edit to suit your purposes. I don't know why you have picked on the BCa method: my experience is that if you need to correct the basic method you often need far more than 1000 samples to get reliable results. You can do the BCa, but you need to supply parameters: z0: typically calculated from the fraction of bootstrap statistics that are = the original statistic acceleration: based on the skewness of the empirical influence function, typically calculated using the jackknife I agree that you should do far more than 1000 samples. The BCa uses bootstrap quantiles that are adjusted based on the z0 and acceleration parameters, and estimating z0 from the bootstrap samples magnifies the Monte Carlo error. You need roughly double as many bootstrap samples as for the bootstrap percentile interval; e.g. 10^4 instead of 5000. If computational expense is an issue, you might prefer bootstrap tilting intervals, which require about 1/37 as many bootstrap samples as the BCa for comparable Monte Carlo variability. Quick overview of confidence intervals: accuracycomments t intervals 1/sqrt(n) Using either formula or bootstrap standard error; poor in the presence of skewness. bootstrap percentile1/sqrt(n) Good quick-and-dirty procedure. Partial skewness correction. Poor if the statistic is biased. bootstrap t 1/n Good coverage, but interval width can vary wildly when n is small. BCa 1/n Current best overall, but you need a lot of bootstrap samples, e.g. 10^4. tilting 1/n Low Monte Carlo variability, so can use fewer bootstrap samples. Difficult to implement, and requires that statistic can be calculated with weights. Advertisement 1: tilting is available in S+Resample, available free from www.insightful.com/downloads/libraries Advertisement 2: I talk about these more in my short course, Bootstrap Methods and Permutation Tests Oct 10-11 San Francisco, 3-4 Oct UK. http://www.insightful.com/services/training.asp | Tim Hesterberg Senior Research Scientist | | [EMAIL PROTECTED] Insightful Corp.| | (206)802-23191700 Westlake Ave. N, Suite 500 | | (206)283-8691 (fax) Seattle, WA 98109-3044, U.S.A. | | www.insightful.com/Hesterberg | __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] bootstrap confidence intervals with previously existing bootstrap sample
Dear R users, I am new to R. I would like to calculate bootstrap confidence intervals using the BCa method for a parameter of interest. My situation is this: I already have a set of 1000 bootstrap replicates created from my original data set. I have already calculated the statistic of interest for each bootstrap replicate, and have also calculated the mean for this statistic across all the replicates. Now I would like to calculate Bca confidence intervals for this statistic. Is there a way to import my previously-calculated set of 1000 statistics into R, and then calculate bootstrap confidence intervals around the mean from this imported data? I have found the code for boot.ci in the manual for the boot package, but it looks like it requires that I first use the boot function, and then apply the output to boot.ci. Because my bootstrap samples already exist, I don't want to use boot, but just want to import the 1000 values I have already calculated, and then get R to calculate the mean and Bca confidence intervals based on these values. Is this possible? Hopefully this makes sense. Thanks so much for any help or advice, Christy Dolph Graduate Student Water Resources Science University of Minnesota-Twin Cities __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] bootstrap confidence intervals with previously existing bootstrap sample
On Tue, 4 Sep 2007, [EMAIL PROTECTED] wrote: Dear R users, I am new to R. I would like to calculate bootstrap confidence intervals using the BCa method for a parameter of interest. My situation is this: I already have a set of 1000 bootstrap replicates created from my original data set. I have already calculated the statistic of interest for each bootstrap replicate, and have also calculated the mean for this statistic across all the replicates. Now I would like to calculate Bca confidence intervals for this statistic. Is there a way to import my previously-calculated set of 1000 statistics into R, and then calculate bootstrap confidence intervals around the mean from this imported data? I have found the code for boot.ci in the manual for the boot package, but it looks like it requires that I first use the boot function, and then apply the output to boot.ci. Because my bootstrap samples already exist, I don't want to use boot, but just want to import the 1000 values I have already calculated, and then get R to calculate the mean and Bca confidence intervals based on these values. Is this possible? Yes, it is possible but you will have to study the internal structure of an object of class boot (which is documented on the help page) and mimic it. You haven't told us which type of bootstrap you used, which is one of the details you need to supply. It might be slightly easier to work with function bcanon in package bootstrap, which you would need to edit to suit your purposes. I don't know why you have picked on the BCa method: my experience is that if you need to correct the basic method you often need far more than 1000 samples to get reliable results. Hopefully this makes sense. Thanks so much for any help or advice, Christy Dolph Graduate Student Water Resources Science University of Minnesota-Twin Cities -- Brian D. Ripley, [EMAIL PROTECTED] Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ University of Oxford, Tel: +44 1865 272861 (self) 1 South Parks Road, +44 1865 272866 (PA) Oxford OX1 3TG, UKFax: +44 1865 272595 __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
