The parametric bootstrap (boot.type = "parametric") for lmer and rlmer
fits now generates both the random effects and the errors anew in every
bootstrap sample, as described in Mason, Cantoni & Ghisletta (2021). In
earlier versions the random effects were held fixed at their predicted
values and only the errors were redrawn, which gave intervals that were too
narrow, most notably for the fixed effects and the random effects' standard
deviations. This affects method = "boot" and method = "BCa". The default
boot.type = "wild", Wald intervals and varComprob fits are not affected.
The wild bootstrap (boot.type = "wild") and the jackknife for
method = "BCa" of lmer and rlmer fits now use independent clusters
when the model has several grouping factors. Earlier versions used the
grouping factor with the most levels, which for nested designs such as
(1 | batch/cask) is the innermost one (cask:batch): the wild bootstrap
gave correlated casks independent weights and understated the variability
between batches, and method = "BCa" failed and returned percentile
intervals with a warning. The clusters are now the coarsest grouping factor
that contains all the others (batch), the same rule as
simulate(type = "wild") in robustlmm. Models with crossed grouping factors
have no independent clusters and now stop with an error unless clusterID
is given. clusterID, the name of a grouping factor or a vector with one
value per observation, is no longer ignored for lmer and rlmer fits with
boot.type = "wild" or method = "BCa". Models with a single grouping
factor give the same results as before (apart from the BCa jackknife fix
below).
The jackknife that estimates the acceleration of method = "BCa" for lmer
and rlmer fits now leaves out one whole cluster at a time. Earlier
versions split the observations by the cluster sizes instead of by cluster,
so each jackknife sample left out a single observation. This changes all
BCa intervals for lmer and rlmer fits; varComprob fits are not
affected.