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POBootstrap two-way large dataset
 

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    <blockquote> <p><strong>Possible Duplicate:</strong><br> <a href="https://stackoverflow.com/questions/13104339/bootstrap-a-large-data-set">Bootstrap a large data set</a> </p> </blockquote> <p>I would like to bootstrap a large two-way data set which contains multiple column and row variables. I have to preserve both row and column variables. The result should be a list containing a bootstrap of all column variables for each row variable. I am providing the required code to answer my question but i think it is not elegant. I would appreciate a better and faster code. The following is a simplified re-creation of the two-way data set:</p> <pre><code>rm(list=ls()) data &lt;- 1:72 </code></pre> <p>Create a two way matrix data:</p> <pre><code>charDataDiff &lt;- matrix(data, nrow=9,ncol=8) varNames &lt;- c("A", "B", "C","A", "B", "C","A", "B", "C") </code></pre> <p>Add a character column to the charDataDiff matrix:</p> <pre><code>charDataDiff &lt;- cbind(varNames ,data.frame(charDataDiff)) </code></pre> <p>Add column names:</p> <pre><code>colnames(charDataDiff) &lt;- c("patchId","s380","s390","s400","s410","s420","s430","s440","s450") </code></pre> <p>Separate the data using the row-variable "patchId" as the criteria. This creates three lists: one for each Variable</p> <pre><code>idColor &lt;- c("A", "B", "C") (patchSpectrum &lt;- lapply(idColor, function(idColor) charDataDiff[charDataDiff$patchId==idColor,])) </code></pre> <p>Created the function sampleBoot to sample the patchSpectrum</p> <pre><code>sampleBoot &lt;- function(nbootstrap=2, patch=3){ return(lapply(1:nbootstrap, function(i) {patchSpectrum[[patch]][sample(1:nrow(patchSpectrum[[patch]]),replace=TRUE),]}))} </code></pre> <p>The list "k" answers my question. However, I think my code is slow for a large data set and large bootstrap. I am only bootstrapping 10 iteration for three row variables. A faster more elegant code is appreciated.</p> <pre><code>numBoots &lt;- 10 for (i in 1: numBoots) k &lt;- lapply(1:3, function(n) do.call(rbind, lapply(sampleBoot(i, n), function(x) apply(x[-1], 2, median)))) k </code></pre>
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    1. COIn your last loop, it looks like `k` is overwritten at each iteration so you are only getting `k` for `i == numBoots`. Is that intended?
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