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    <p>In general implicit loops are faster the explicit loops. Try taking the code inside your loop and place it in a function, and then using that function in an lapply, or sapply statement.</p> <pre><code>myfunction = function(&lt;insert relevant parameters here&gt;) { X.sample &lt;- X[ sample(1:nrow(X), 40, replace = FALSE), ] X.sample.1 &lt;- X.sample[1:20, ] X.sample.2 &lt;- X.sample[21:40, ] Y &lt;- as.data.frame(cbind(X.sample.1$ID, X.sample.1$x, X.sample.2$ID, X.sample.2$x)) cor.results &lt;- cor.test(Y[,2], Y[,4], alternative = c("greater"), method = c("pearson")) cor.results$estimate } Z = sapply(x, myfunction) #Here every element of x contains the arguments you want to pass to my function #You can pass multiple arguments separated by commas after the function name error &lt;- qt(0.975, df = (length(Z) - 1)) * (sd(Z))/sqrt(length(Z)) </code></pre> <p>You could do this, but I find it's probably better to just use the <code>boot()</code> function in the <code>boot</code> package if you can.</p> <p>As for the <code>set.seed()</code> You need to set it directly before EVERY time you generate random anything. See below.</p> <pre><code>&gt; rnorm(6) [1] 1.0915017 -0.6229437 -0.9074604 -1.5937133 0.3026445 1.6343924 &gt; set.seed(1001) &gt; rnorm(6) [1] 2.1886481 -0.1775473 -0.1852753 -2.5065362 -0.5573113 -0.1435595 &gt; set.seed(1001) &gt; rnorm(6) [1] 2.1886481 -0.1775473 -0.1852753 -2.5065362 -0.5573113 -0.1435595 &gt; rnorm(6) [1] 1.0915017 -0.6229437 -0.9074604 -1.5937133 0.3026445 1.6343924 &gt; set.seed(1001) &gt; sample(1:5,10,replace=T) [1] 5 3 3 3 3 5 1 1 2 4 &gt; sample(1:5,10,replace=T) [1] 3 1 5 3 2 5 1 2 1 4 &gt; set.seed(1001) &gt; sample(1:5,10,replace=T) [1] 5 3 3 3 3 5 1 1 2 4 &gt; rnorm(6) [1] -0.1435595 1.0915017 -0.6229437 -0.9074604 -1.5937133 0.3026445 &gt; set.seed(1001) &gt; rnorm(6) [1] 2.1886481 -0.1775473 -0.1852753 -2.5065362 -0.5573113 -0.1435595 </code></pre> <p>Hope that helps!</p> <p>When researching the <code>boot</code> function to give you an example I ran into a snag. It only ever returns one row. Strange! I might start a new question about this. In anycase, I think the <code>bootstrap()</code> function in the <code>bootstrap</code> package will do what you are looking for. Here's my example</p> <pre><code>set.seed(1001) X &lt;- rnorm(600, 7, 2) myStat &lt;- function(x, pairs) { index = sample(1:length(x),(pairs*2)) Z = cor(X[index[1:(length(index)/2)]], X[index[((length(index)/2)+1):length(index)]]) return(Z) } b=bootstrap(X,1000,myStat,pairs=20) Z &lt;- b$thetastar error &lt;- qt(0.975, length(Z)-1 * sd(Z)/sqrt(length(Z))) </code></pre>
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    1. POSimulated dataset for boot strapping analysis
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    1. USDavy Kavanagh
    1. USDavy Kavanagh
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    1. COThanks for the good advice. Can you make a suggestion of how to create the dataset (note good advice above the ID field is completely unnecessary) and use the boot command? I tried and that is why I feel back on the less than perfect for-loop. Thanks, Keith
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    2. COSee edits above!
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