Aggregate multiseed VIM results

Usage

aggregate_vim(result_list, agg_method, ci_grid, n_eff, alpha = 0.05)

result_list: List of result data frames return by the vim function.
agg_method: P-value aggregation method use to combine results from different seeds. Current options are "bonferroni" (Bonferroni's method), "hommel" (Hommel's method), "arithmetic" (arithmetic mean), "geometric" (geometric mean), "harmonic" (harmonic mean), "compound_bg" (compound Bonferroni and geometric mean), and "compound_ba" (compound Bonferroni and arithmetic mean). These approaches are discussed at length in Vovk and Wang (2020). Defaults to "compound_bg", which has been shown to work well in many settings.
ci_grid: Grid of VIM values over which to construct a confidence interval by inverting a hypothesis test. The aggregation works by constructing hypothesis tests (at level alpha) of the null corresponding to each value in ci_grid, and then inverting these tests to yield a 1 - alpha confidence interval. For example, for "AUC" importance, the VIM takes values in (0,1), so a grid of values between 0 and 1 would be a reasonable choice.
n_eff: The effective sample size. Without sample-splitting, this is simply the sample size. With sample-splitting, this is the sample size divided by two (i.e., the size of each of the two halves of the data).
alpha: The level at which to compute confidence intervals and hypothesis tests. Defaults to 0.05.

Named list with the following elements:

agg_result: Data frame giving results aggregated over seeds.
agg_method: P-value aggregation method used.
n_seed: Number of iterations (seeds) used to perform the VIM estimation procedure.
vim_objects: A list of vim return objects, each corresponding to a different seed.