Compute bootstrap-based standard error estimates for variable importance
bootstrap_se(
Y = NULL,
f1 = NULL,
f2 = NULL,
type = "r_squared",
b = 1000,
boot_interval_type = "perc",
alpha = 0.05
)a bootstrap-based standard error estimate
the outcome.
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.
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.
the type of importance to compute; defaults to
r_squared, but other supported options are auc,
accuracy, deviance, and anova.
the number of bootstrap replicates (only used if bootstrap = TRUE
and sample_splitting = FALSE); defaults to 1000.
the type of bootstrap interval (one of "norm",
"basic", "stud", "perc", or "bca", as in
boot{boot.ci}) if requested. Defaults to "perc".
the level to compute the confidence interval at. Defaults to 0.05, corresponding to a 95% confidence interval.