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  1. PO
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    <p>You just need to look inside <code>plm:::summary.plm</code> to see what it is doing. When you do, you'll see that your two lines calling <code>summary()</code> on your model fit can be replaced with:</p> <pre><code>coefficients_estimated &lt;- coef(regression) ses_estimated &lt;- sqrt(diag(vcov(regression))) </code></pre> <p>For example:</p> <pre><code>require(plm) data("Produc", package = "plm") zz &lt;- plm(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, data = Produc, index = c("state","year")) </code></pre> <p><code>summary(zz)</code> gives:</p> <pre><code>&gt; summary(zz) Oneway (individual) effect Within Model .... Coefficients : Estimate Std. Error t-value Pr(&gt;|t|) log(pcap) -0.02614965 0.02900158 -0.9017 0.3675 log(pc) 0.29200693 0.02511967 11.6246 &lt; 2.2e-16 *** log(emp) 0.76815947 0.03009174 25.5273 &lt; 2.2e-16 *** unemp -0.00529774 0.00098873 -5.3582 1.114e-07 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 .... </code></pre> <p>and the two lines I showed return for <code>zz</code>:</p> <pre><code>&gt; coef(zz) log(pcap) log(pc) log(emp) unemp -0.026149654 0.292006925 0.768159473 -0.005297741 &gt; sqrt(diag(vcov(zz))) log(pcap) log(pc) log(emp) unemp 0.0290015755 0.0251196728 0.0300917394 0.0009887257 </code></pre> <p>You don't really provide enough information (your simulation code nor the full output from <code>Rprof()</code> for example) to say whether this will help - it certainly doesn't look like vast amounts of time are spent in <code>summary()</code>; <code>FUN</code> is far more costly than anything else you show, and of the elements you do show, <code>r.squared()</code> is the only one that appears in <code>plm:::summary.plm()</code> and it seems to take no time at all.</p> <p>So, whether the above speeds things up appreciably remains to be seen.</p>
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    1. USGavin Simpson
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    1. COYou're right, there is a negligible effect on speed. As I noted in my comment on the question, I had a significant increase from reducing to one call to summary (~30% reduction in time). I didn't think to look in to what summary.plm was doing, so I appreciate that insight for future problems. Regarding your advice to look at "FUN", I'm not sure what "FUN" is refering to. I would think an argument to apply, but I don't have any apply loops... Is there any way to easily find out what "FUN" is? In any case, answer accepted. Thank you.
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    2. CO@Wilduck `FUN` is usually the argument name for a function that is to be applied to chunks of data. We don't have your simulation code, so it is difficult to say what `FUN` is, but are you using any of the `apply()`, `sapply()`, `lapply()` family etc?
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      1. USGavin Simpson
    3. COyeah, that's pretty much what I thought. I'm not using any of the apply family on large chunks of data, so it must be something else. I'm not going to make you go over my simulation code before I've gone through it with a fine toothed comb. Thanks for all your help.
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