dfl_decompose_bootstrap: Bootstrapping the DFL reweighting decomposition

Description

The function resamples observations and restimates the DFL decomposition with the new sample.

Usage

dfl_decompose_bootstrap(
  formula,
  dep_var,
  data_used,
  weights,
  group_variable,
  reference_group,
  estimate_statistics,
  statistics,
  probs,
  custom_statistic_function,
  right_to_left,
  trimming,
  trimming_threshold,
  ...
)

Arguments

formula: formula object
dep_var: dependent variable
data_used: data.frame with data used for estimation
weights: weights variable
group_variable: group variable
reference_group: reference_group to be reweighted
estimate_statistics: boolean: if TRUE (default), then distributional statistics are estimated and the decomposition is performed. If FALSE, the function only returns the fitted inverse propensity weights.
statistics: a character vector that defines the distributional statistics for which the decomposition is performed.
probs: a vector of length 1 or more with the probabilities of the quantiles to be estimated.
custom_statistic_function: a function estimating a custom distributional statistic that will be decomposed.
right_to_left: determines the direction of a sequential decomposition.
trimming: boolean: If TRUE, observations with dominant reweighting factor values are trimmed according to rule of Huber, Lechner, and Wunsch (2013).
trimming_threshold: numeric: threshold defining the maximal accepted relative weight of the reweighting factor value (i.e., inverse probability weight) of a single observation. If NULL, the threshold is set to \(sqrt(N)/N\), where \(N\) is the number of observations in the reference group.
...: other parameters passed to the function estimating the conditional probabilities.