Bootstrap a statistical model n times to return a data frame of estimates.
bootstrap_model(model, iterations = 1000, ...) # S3 method for default bootstrap_model( model, iterations = 1000, type = "ordinary", parallel = c("no", "multicore", "snow"), n_cpus = 1, verbose = FALSE, ... ) # S3 method for merMod bootstrap_model( model, iterations = 1000, type = "parametric", parallel = c("no", "multicore", "snow"), n_cpus = 1, verbose = FALSE, ... )
The number of draws to simulate/bootstrap.
Arguments passed to or from other methods.
Character string specifying the type of bootstrap. For mixed models
The type of parallel operation to be used (if any).
Number of processes to be used in parallel operation.
Toggle warnings and messages.
A data frame of bootstrapped estimates.
boot::boot() is used to generate bootstraps from
the model data, which are then used to
update() the model, i.e. refit
the model with the bootstrapped samples. For
merMod objects (lme4)
or models from glmmTMB, the
lme4::bootMer() function is used to
obtain bootstrapped samples.
bootstrap_parameters() summarizes the
bootstrapped model estimates.
The output can be passed directly to the various functions from the
emmeans package, to obtain bootstrapped estimates, contrasts, simple
slopes, etc. and their confidence intervals. These can then be passed to
model_parameter() to obtain standard errors, p-values, etc (see
Note that that p-values returned here are estimated under the assumption of translation equivariance: that shape of the sampling distribution is unaffected by the null being true or not. If this assumption does not hold, p-values can be biased, and it is suggested to use proper permutation tests to obtain non-parametric p-values.