CGR Bootstrap for Nested LMEs
Generate semi-parametric bootstrap replicates of a statistic for a nested linear mixed-effects model.
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The model object you wish to bootstrap.
A function returning the statistic(s) of interest.
The number of bootstrap resamples.
The semi-parametric bootstrap algorithm implemented was outlined by Carpenter, Goldstein and Rasbash (2003). The algorithm is outlined below:
Obtain the parameter estimates from the fitted model and calculate the estimated error terms and EBLUPs.
Rescale the error terms and EBLUPs so that the empirical variance of these quantities is equal to estimated variance components from the model.
Sample independently with replacement from the rescaled estimated error terms and rescaled EBLUPs.
Obtain bootstrap samples by combining the samples via the fitted model equation.
Refit the model and extract the statistic(s) of interest.
Repeat steps 3-5 B times.
The returned value is an object of class "boot", compatible with the boot
Carpenter, J. R., Goldstein, H. and Rasbash, J. (2003) A novel bootstrap procedure for assessing the relationship between class size and achievement. Journal of the Royal Statistical Society. Series C (Applied Statistics), 52, 431–443.
reb_bootstrapfor more details on a specific bootstrap.
bootMerin the lme4 package for an implementation of (semi-)parameteric bootstrap for mixed models.
plot.bootfrom the boot package.
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