9
$\begingroup$

Consider an experiment with multiple human participants, each measured multiple times in two conditions. A mixed effects model can be formulated (using lme4 syntax) as:

fit = lmer(
    formula = measure ~ (1|participant) + condition
)

Now, say I want to generate bootstrapped confidence intervals for the predictions of this model. I think I've come up with a simple and computationally efficient method, and I'm sure I'm not the first to think of it, but I'm having trouble finding any prior publications describing this approach. Here it is:

  1. Fit the model (as above), call this the "original model"
  2. Obtain predictions from the original model, call these the "original predictions"
  3. Obtain residuals from the original model associated with each response from each participant
  4. Resample the residuals, sampling participants with replacement
  5. Fit a linear mixed effects model with gaussian error to the residuals, call this the "interim model"
  6. Compute predictions from the interim model for each condition (these predictions will be very close to zero), call these the "interim predictions"
  7. Add the interim predictions to the original predictions, call the result the "resample predictions"
  8. Repeat steps 4 through 7 many times, generating a distribution of resample predictions for each condition from which once can compute CIs.

I've seen "residual bootstrapping" procedures in the context of simple regression (i.e. not a mixed model) where residuals are sampled as the unit of resampling and then added to the predictions of the original model before fitting a new model on each iteration of the bootstrap, but this seems rather different from the approach I describe where residuals are never resampled, people are, and only after the interim model is obtained do the original model predictions come into play. This last feature has a really nice side-benefit in that no matter the complexity of the original model, the interim model can always be fit as a gaussian linear mixed model, which can be substantially faster in some cases. For example, I recently had binomial data and 3 predictor variables, one of which I suspected would cause strongly non-linear effects, so I had to employ Generalized Additive Mixed Modelling using a binomial link function. Fitting the original model in this case took over an hour, whereas fitting the gaussian LMM on each iteration took mere seconds.

I really don't want to claim priority on this if it's already a known procedure, so I'd be very grateful if anyone can provide information on where this might have been described before. (Also, if there are any glaring problems with this approach, do let me know!)

$\endgroup$
  • 1
    $\begingroup$ Just a side comment, but it might be relevant. Peter McCullagh has a paper in Bernoulli where he shows that no bootstrap correctly estimates the variance in a random effects model. $\endgroup$ – cardinal Mar 30 '11 at 17:02
  • $\begingroup$ @Mike (+1) That is one very well written question! $\endgroup$ – chl Mar 30 '11 at 18:30
  • 1
    $\begingroup$ Why wouldn't you resample participants with replacement and then resample their data as well? That seems to be more in keeping of the spirit of a multilevel model with one distribution nested within another. Another point is that there is a potential problem with binomial data because the extreme ends of the samples will be less likely to converge. $\endgroup$ – John Mar 30 '11 at 19:09
  • $\begingroup$ @John: I prefer resampling residuals because (1) it is faster for when the original model is laborious to estimate, and (2) it yields CIs that have removed variability attributable to variability between participant means. #2 means that you don't have to create multiple plots when you want to show raw data and a repeated-measures effect; you can just plot these between-variance-removed CIs on the raw data and they will be appropriate for comparison of conditions repeated within individuals. Granted there may be confusion on the meaning of such CIs, but that's what figure captions are for. $\endgroup$ – Mike Lawrence Mar 30 '11 at 19:25
  • $\begingroup$ @John: Could you elaborate on your fears regarding the applicability of this approach to binomial data? $\endgroup$ – Mike Lawrence Mar 30 '11 at 19:26
-1
$\begingroup$

My book Bootstrap Methods 2nd Edition has a massive bibliography up to 2007. So even if I don't cover the subject in the book the reference might be in the bibliography. Of course a Google search with the right key words might be better. Freedman, Peters and Navidi did bootstrapping for prediction in linear regression and econometric models but I am not sure what has been done on the mixed model case. Stine's 1985 JASA paper Bootstrap prediction intervals for regression is something you will find very interesting if you haven't already seen it.

$\endgroup$

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.