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First of all, I am not a statistician. I only how to interpret stats and to do them with R, my understanding of the math/formulas behind them is virtually zero.

With this said, I am looking for a laymen's explanation of the way percentile bootstrap confidence intervals are calculated for mixed-effects models with the confint() function of the R package lme4 (that is, I would like to gain a basic understanding of how the math works)

Would it be correct to state that this function selects a user-defined number of subsamples from the original data, applies the regression model to them and then calculates the range within which we can be 95% sure that the true population effect of a level of a predictor falls? How does this calculation work? Would it be correct to portray it as averaging over a large number of coefficients?

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  • $\begingroup$ possible duplicate of Explaining to laypeople why bootstrapping works $\endgroup$ – StasK Apr 29 '15 at 15:12
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    $\begingroup$ Wow, you are in a lot of trouble if you don't understand statistics, yet are trying to apply two complicated statistical procedures (mixed models and the bootstrap) together. For mixed models, the bootstrap needs to respect the non-i.i.d. nature of the data, so if you just apply the bootstrap as is, you will reproduce the standard errors and the confidence intervals that you would get from glm rather than glmer. $\endgroup$ – StasK Apr 29 '15 at 15:14
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    $\begingroup$ Comments like yours are the reason I'm stressed out when I have to visit this site. Zero respect for people who only apply stats and an unstoppable reflex to flag topics that are not even remotely related as issues. I am sure the creators of LME4 (who have also designed the bootstrap confint function) will have thought of the issue you mention. $\endgroup$ – Jeroen Claes Apr 29 '15 at 18:50
  • $\begingroup$ What I am saying is that you would have been in a better position if you knew the theory behind both of these. Otherwise, a lot of issues that can screw up your analysis will slip your attention. You may be in a better position finding a collaborator who understands statistics, so that your research results don't get rebutted. My reading of lmer documentation is that confint.merMod in lme4 is parametric bootstrap. It does not resample the data, as was nicely explained by Maarten, but instead it simulates the data from the distribution created by the model with the estimated parameters. $\endgroup$ – StasK Apr 30 '15 at 2:54
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A percentile bootstrap confidence interval starts by taking the definition of a 95% confidence interval literally: If we were to draw repeatedly random samples (with replacement) from our population and computed a coefficient in each of these samples, then 95% of these coefficients would fall within that interval. Problem is that it is usually impossible/unpractical to repeatedly sample from the population. We do have a good approximation of the population: our current sample. So the bootstrap repeatedly samples $N$ out of $N$ observations with replacement (so some observations appear twice, some three time or even more, and some won't appear at all) and computes the coefficients of interest in each of these samples. The percentile confidence interval orders these coeffcients from smallest to largest and the lower bound is chosen such that the there are only 2.5% of the coefficients smaller than that lower bound and the upper bound is chosen such that there are only 2.5% larger than that upper bound (these are the 2.5$^{\mathrm{th}}$ and 97.5$^{\mathrm{th}}$ percentiles.

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