bootstrap_estimate_ob_decompose: Bootstrapping the OB decomposition

Description

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

Usage

bootstrap_estimate_ob_decompose(
  formula_decomposition,
  formula_reweighting,
  data_used,
  group,
  reference_0,
  normalize_factors,
  reweighting,
  reweighting_method,
  trimming,
  trimming_threshold,
  rifreg,
  rifreg_statistic,
  rifreg_probs,
  custom_rif_function,
  na.action,
  cluster = NULL,
  ...
)

Arguments

formula_decomposition: formula object that contains the formula for the decomposition
formula_reweighting: formula object that contains the formula for the reweighting in case of a reweighted decompostion
data_used: data.frame with data used for estimation (including weight and group variable)
group: name of the a binary variable (numeric or factor) identifying the two groups that will be compared. The group identified by the lower ranked value in `group` (i.e., 0 in the case of a dummy variable or the first level of factor variable) is defined as group 0.
reference_0: boolean: indicating if group 0 is the reference group and if its coefficients are used to compute the counterfactual mean.
normalize_factors: boolean: If `TRUE`, then factor variables are normalized as proposed by Gardeazabal/Ugidos (2004)
reweighting: boolean: if `TRUE`, then the decomposition is performed with with respect to reweighted reference group.
reweighting_method: specifies the method fit and predict conditional probabilities used to derive the reweighting factor. Currently, "logit", "fastglm", and "random_forest" are available.
trimming: boolean: If TRUE, observations with dominant reweighting factor values are trimmend according to rule of Huber, Lechner, and Wunsch (2013). Per default, trimming is set to FALSE.
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.
rifreg: boolean: if `TRUE`, then RIF decomposition is performed
rifreg_statistic: string containing the distributional statistic for which to compute the RIF.
rifreg_probs: a vector of length 1 or more with probabilities of quantiles.
custom_rif_function: the RIF function to compute the RIF of the custom distributional statistic.
na.action: generic function that defines how NAs in the data should be handled.
cluster: numeric vector of same length as dep_var indicating the clustering of observations. If cluster = NULL (default), no clustering is a assumend and bootstrap procedure resamples individual observations. Otherwise bootstrap procedure resamples clusters.
...: additional parameters passed to custom_rif_function