assess: Assess Generalizability of Randomized Sample to Population

In NUstat/generalizeR: Design a Sample Recruitment Plan and Assess Its 'Generalizability' to Broader Populations

Assess Generalizability of Randomized Sample to Population

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(
  data,
  guided = TRUE,
  sample_indicator,
  covariates,
  estimation_method = "lr",
  disjoint_data = TRUE,
  trim_pop = FALSE
)

Arguments

data	data frame comprised of "stacked" sample and target population data
guided	logical. Default is TRUE. If FALSE, then user must enter all arguments in function to bypass guided mode
sample_indicator	variable name denoting sample membership (1 = in sample, 0 = out of sample)
covariates	vector of covariate names in data set that predict sample membership
estimation_method	method to estimate the probability of sample membership (propensity scores). Default is logistic regression ("lr"). Other methods supported are Random Forests ("rf") and Lasso ("lasso")
disjoint_data	logical. If TRUE, then sample and population data are considered disjoint. 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 sample covariates.

Value

returns generalizeR_assess object that includes the generalizability index, propensity scores, and a covariate table

NUstat/generalizeR documentation built on Feb. 7, 2024, 6:39 p.m.