bootstrap.merMod | R Documentation |
Perform various bootstrap process for nested linear mixed effects (LMEs) models including: parametric, residual, cases, wild, and REB bootstraps.
## S3 method for class 'merMod' bootstrap( model, .f = extract_parameters, type, B, resample, reb_type, hccme, aux.dist, orig_data = NULL, .refit = TRUE ) ## S3 method for class 'lme' bootstrap( model, .f = extract_parameters, type, B, resample, reb_type, hccme, aux.dist, orig_data = NULL, .refit = TRUE ) bootstrap( model, .f, type, B, resample = NULL, reb_type = NULL, hccme = NULL, aux.dist = NULL, orig_data = NULL, .refit = TRUE )
model |
The model object you wish to bootstrap. |
.f |
A function returning the statistic(s) of interest. |
type |
A character string indicating the type of bootstrap that is being
requested. Possible values are |
B |
The number of bootstrap resamples. |
resample |
A logical vector specifying whether each level of the model should be resampled in the cases bootstrap. The levels should be specified from the highest level (largest cluster) of the hierarchy to the lowest (observation-level); for example for students within a school, specify the school level first, then the student level. |
reb_type |
Specification of what random effect block bootstrap version to
implement. Possible values are |
hccme |
either |
aux.dist |
one of |
orig_data |
the original data frame. This should be specified if variables
are transformed within the formula for |
.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 |
All of the below methods have been implemented for nested linear mixed-effects
models fit by lmer
(i.e., an lmerMod
object) and lme
(i.e., an lmerMod
object). Details of the bootstrap procedures can be found
in the help file for that specific function.
The returned value is an object of class "lmeresamp". This is a list with the following elements:
observed
: the estimated values for the model parameters
model
: the fitted model object
.f
: the function call
replicates
: a B \times p data frame of bootstrap values for each of the p model parameters,
stats
: a tibble containing the \]codeobserved, rep.mean
(bootstrap mean),
se
(bootstrap standard error), and bias
values for each model parameter,
B
: the number of bootstrap resamples performed
data
: the data with which the model was fit
seed
: a vector of randomly generated seeds that are used by the bootstrap
type
: the type of bootstrap executed
call
: the call to bootstrap()
that the user
message
: a list of length B
giving any messages generated during refitting. An entry will be NULL
if no message was generated.
warning
: a list of length B
giving any warnings generated during refitting. An entry will be NULL
if no message was generated.
error
: a list of length B
giving any errors generated during refitting. An entry will be NULL
if no message was generated.
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.
Chambers, R. and Chandra, H. (2013) A random effect block bootstrap for clustered data. Journal of Computational and Graphical Statistics, 22, 452–470.
Morris, J. S. (2002) The BLUPs are not "best" when it comes to bootstrapping. Statistics and Probability Letters, 56, 425–430.
Van der Leeden, R., Meijer, E. and Busing F. M. (2008) Resampling multilevel models. In J. de Leeuw and E. Meijer, editors, Handbook of Multilevel Analysis, pages 401–433. New York: Springer.
Bates, D., Maechler, M., Bolker, W., Walker, S. (2015). Fitting Linear Mixed-Effects Models Using lme4. Journal of Statistical Software, 67, 1–48. doi:10.18637/jss.v067.i01.
Modugno, L., & Giannerini, S. (2015). The Wild Bootstrap for Multilevel Models. Communications in Statistics – Theory and Methods, 44(22), 4812–4825.
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.
library(lme4) vcmodA <- lmer(mathAge11 ~ mathAge8 + gender + class + (1 | school), data = jsp728) ## you can write your own function to return stats, or use something like 'fixef' mySumm <- function(.) { s <- getME(., "sigma") c(beta = getME(., "beta"), sigma = s, sig01 = unname(s * getME(., "theta"))) } ## running a parametric bootstrap set.seed(1234) boo1 <- bootstrap(model = vcmodA, .f = mySumm, type = "parametric", B = 20) ## to print results in a formatted way print(boo1) ## Not run: ## running a cases bootstrap - only resampling the schools boo2 <- bootstrap(model = vcmodA, .f = mySumm, type = "case", B = 100, resample = c(TRUE, FALSE)) ## running a cases bootstrap - resampling the schools and students within the school boo3 <- bootstrap(model = vcmodA, .f = mySumm, type = "case", B = 100, resample = c(TRUE, TRUE)) ## running a residual bootstrap boo4 <- bootstrap(model = vcmodA, .f = mySumm, type = "residual", B = 100) ## running an REB0 bootstrap boo5 <- bootstrap(model = vcmodA, .f = mySumm, type = "reb", B = 100, reb_typ = 0) ## Running the Wild bootstrap boo6 <- bootstrap(model = vcmodA, .f = mySumm, type = "wild", B= 100, hccme = "hc2", aux.dist = "mammen") ## Running a bootstrap in parallel via foreach library(foreach) library(doParallel) set.seed(1234) numCores <- 2 cl <- makeCluster(numCores, type = "PSOCK") # make a socket cluster doParallel::registerDoParallel(cl) # how the CPU knows to run in parallel b_parallel <- foreach(B = rep(250, 2), .combine = combine_lmeresamp, .packages = c("lmeresampler", "lme4")) %dopar% { bootstrap(vcmodA, .f = fixef, type = "parametric", B = B) } stopCluster(cl) ## Running a bootstrap in parallel via parLapply cl <- makeCluster(numCores, type = "PSOCK") # make a socket cluster doParallel::registerDoParallel(cl) # how the CPU knows to run in parallel boot_mod <- function(...) { library(lme4) library(lmeresampler) vcmodA <- lmer(mathAge11 ~ mathAge8 + gender + class + (1 | school), data = jsp728) bootstrap(vcmodA, .f = fixef, type = "parametric", B = 250) } result <- parLapply(cl, seq_len(2), boot_mod) b_parallel2 <- do.call("combine_lmeresamp", result) stopCluster(cl) ## End(Not run)
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