R: Wild Bootstrap for LME models

wild_bootstrap.lmerMod {lmeresampler}

R Documentation

Wild Bootstrap for LME models

Description

Generate wild bootstrap replicates of a statistic for a linear mixed-effects model.

Usage

## 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)

Arguments

`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 `"hc2"` or `"hc3"`, indicating which heteroscedasticity consistent covariance matrix estimator to use.
`aux.dist`	one of `"mammen"`, `"rademacher"`, `"norm"`, `"webb"`, or `"gamma"` indicating which auxiliary distribution to draw the errors from
`.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 `TRUE`.

Details

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.

Value

The returned value is an object of class "lmeresamp".

References

Modugno, L., & Giannerini, S. (2015). The Wild Bootstrap for Multilevel Models. Communications in Statistics – Theory and Methods, 44(22), 4812–4825.