<p><div><div><div><div data-show-faces="false" data-height="21" data-width="100" data-layout="button_count" data-send="true" data-href="https://www.r-bloggers.com/lmer-vs-inla-for-variance-components/"></div></div><div></div></div><div></div></div>
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(This article was first published on <strong><a href="https://aghaynes.wordpress.com/2018/11/24/lmer-vs-inla/"> R – Insights of a PhD</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
<p>Just for fun, I decided to compare the estimates from lmer and INLA for the variance components of an LMM (this isn’t really something that you would ordinarily do – comparing frequentist and bayesian approaches). The codes are below. A couple of plots are drawn, which show the distribution of the hyperparameters (in this case variances) from INLA, which are difficult to get from the frequentist framework (there’s a link to a presentation by Douglas Bates in the code, detailing why you might not want to do it [distribution is not symmetrical], and how you could do it… but it’s a lot of work).</p>
<p>Note that we’re comparing the precision (tau) rather than the variance or SD… SD = 1/sqrt(tau)</p>
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<p>As you’d hope, the results come pretty close to each other and the truth:</p>
<pre><span>cbind(truth = c(tau, tau.ri), lmer = 1/c(attr(vc, &quot;sc&quot;)^2, unlist(vc)), inla = imod$summary.hyperpar$`0.5quant`)
</span><span> truth lmer inla
3.00 2.9552444 2.9556383
group 0.25 0.2883351 0.2919622</span></pre>
<p>Code on <a rel="nofollow" href="https://gist.github.com/aghaynes/8c9decf94acdc64d0107222f1f453803" target="_blank">Github…</a></p>
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<div>To <strong>leave a comment</strong> for the author, please follow the link and comment on their blog: <strong><a href="https://aghaynes.wordpress.com/2018/11/24/lmer-vs-inla/"> R – Insights of a PhD</a></strong>.</div>
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