wild_bootstrap.lmerMod | R Documentation |
Generate wild bootstrap replicates of a statistic for a linear mixed-effects model.
## S3 method for class 'lmerMod'
wild_bootstrap(
model,
.f,
B,
hccme = c("hc2", "hc3"),
aux.dist = c("mammen", "rademacher", "norm", "webb", "gamma"),
.refit = TRUE
)
## S3 method for class 'lme'
wild_bootstrap(
model,
.f,
B,
hccme = c("hc2", "hc3"),
aux.dist = c("mammen", "rademacher", "norm", "webb", "gamma"),
.refit = TRUE
)
wild_bootstrap(model, .f, B, hccme, aux.dist, .refit = TRUE)
model |
The model object you wish to bootstrap. |
.f |
A function returning the statistic(s) of interest. |
B |
The number of bootstrap resamples. |
hccme |
either |
aux.dist |
one of |
.refit |
a logical value indicating whether the model should be refit to
the bootstrap resample, or if the simulated bootstrap resample should be
returned. Defaults to |
The wild bootstrap algorithm for LMEs implemented here was outlined by Modugno & Giannerini (2015). The algorithm is outlined below:
Draw a random sample equal to the number of groups (clusters) from
an auxillary distribution with mean zero and unit variance.
Denote these as w_1, \ldots, w_g
.
Calculate the selected heteroscedasticity consistent matrix estimator for
the marginal residuals, \tilde{v}_i
Generate bootstrap responses using the fitted equation:
y_i^* = X_i \beta + \tilde{v}_i w_j
Refit the model and extract the statistic(s) of interest.
Repeat steps 2-4 B times.
The returned value is an object of class "lmeresamp".
Modugno, L., & Giannerini, S. (2015). The Wild Bootstrap for Multilevel Models. Communications in Statistics – Theory and Methods, 44(22), 4812–4825.
Examples are given in bootstrap
parametric_bootstrap
, resid_bootstrap
,
case_bootstrap
, reb_bootstrap
,
wild_bootstrap
for more details on a specific bootstrap.
bootMer
in the lme4 package for an
implementation of (semi-)parametric bootstrap for mixed models.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.