weighting: Estimate Weights for Generalizing Average Treatment Effect

In katiecoburn/generalizeR: What the Package Does (One Line, Title Case)

Description

This function is designed for use within 'covariate_table()' and 'assess()'.

Usage

weighting(
  outcome,
  treatment,
  trial,
  selection_covariates,
  data,
  selection_method = "lr",
  is_data_disjoint = TRUE,
  seed
)

Arguments

outcome	variable name denoting outcome
treatment	variable name denoting binary treatment assignment (ok if only available in trial, not population)
trial	variable name denoting binary trial participation (1 = trial participant, 0 = not trial participant)
selection_covariates	vector of covariate names in data set that predict trial participation
data	data frame comprised of "stacked" trial and target population data
selection_method	method to estimate the probability of trial participation. Default is logistic regression ("lr"). Other methods supported are Random Forests ("rf") and Lasso ("lasso")
is_data_disjoint	logical. If TRUE, then trial and population data are considered independent. This affects calculation of the weights - see details for more information.
seed	numeric. By default, the seed is set to 13783, otherwise can be specified (such as for simulation purposes).

katiecoburn/generalizeR documentation built on Oct. 28, 2020, 4:43 a.m.