generateCovariates: Generate a data frame with 10 covariates following a...

In kkbrum/PSMOptimalCalipers: Reproduces and Extends Simulations on Optimal Caliper Width in Propensity-Score Matching

Description

Our simulations involve 10 covariates, which are either 1) 10 independent standard normal covariates, 2) 10 standard normal covariates that have pairwise correlations of 0.25, 3) 5 binary Bernouilli covariates with success probability 0.5 and 5 independent standard normal covariates, 4) 9 binary Bernouilli covariates with success probability 0.5 and 1 standard normal covariate, or 5) 10 binary Bernouilli covariates with success probability 0.5. These are abbreviated by "Ind Norm", "Corr Norm", "5 Bern", "9 Bern", and "10 Bern", respectively, and can also be referred to by their number (1-5).

Usage

generateCovariates(cov_dist, n = 5000)

Arguments

cov_dist	The covariate distribution. An integer 1-5 or one of the following: 'Ind Norm', 'Corr Norm', '5 Bern', '9 Bern', '10 Bern'
n	The number of rows or observations to be generated

Value

Returns a data frame with 10 covariates of the specified distributions and n rows.

kkbrum/PSMOptimalCalipers documentation built on Dec. 21, 2021, 6:45 a.m.