Compute bootstrap-based standard error estimates for variable importance

bootstrap_se(
  Y = NULL,
  f1 = NULL,
  f2 = NULL,
  cluster_id = NULL,
  clustered = FALSE,
  type = "r_squared",
  b = 1000,
  boot_interval_type = "perc",
  alpha = 0.05
)

Arguments

Y: the outcome.
f1: the fitted values from a flexible estimation technique regressing Y on X. A vector of the same length as Y; if sample-splitting is desired, then the value of f1 at each position should be the result of predicting from a model trained without that observation.
f2: the fitted values from a flexible estimation technique regressing either (a) f1 or (b) Y on X withholding the columns in indx. A vector of the same length as Y; if sample-splitting is desired, then the value of f2 at each position should be the result of predicting from a model trained without that observation.
cluster_id: vector of the same length as Y giving the cluster IDs used for the clustered bootstrap, if clustered is TRUE.
clustered: should the bootstrap resamples be performed on clusters rather than individual observations? Defaults to FALSE.
type: the type of importance to compute; defaults to r_squared, but other supported options are auc, accuracy, deviance, and anova.
b: the number of bootstrap replicates (only used if bootstrap = TRUE and sample_splitting = FALSE); defaults to 1000.
boot_interval_type: the type of bootstrap interval (one of "norm", "basic", "stud", "perc", or "bca", as in boot.ci) if requested. Defaults to "perc".
alpha: the level to compute the confidence interval at. Defaults to 0.05, corresponding to a 95% confidence interval.

Value

a bootstrap-based standard error estimate