assess: Assess Generalizability of Randomized Trial to Population

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

Description

This function, given a stacked data frame containing both sample and population data, assesses the generalizability of the sample to the population on given covariates.

Usage

assess(
  trial,
  selection_covariates,
  data,
  selection_method = "lr",
  is_data_disjoint = TRUE,
  trim_pop = FALSE,
  seed = 12222
)

Arguments

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.
trim_pop	logical. If TRUE, then population data are subset to exclude individuals with covariates outside bounds of trial covariates.
seed	numeric. By default, the seed is set to 12222, otherwise can be specified (such as for simulation purposes).

Details

'assess_wrap()' is a wrapper for this function that allows assessment over levels of a grouping variable.

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