glm.simplereg.ps: Propensity score estimation with a linear model

In precmed: Precision Medicine

Propensity score estimation with a linear model

Description

Propensity score based on a multivariate logistic regression with main effects only

Usage

glm.simplereg.ps(trt, x.ps, xnew = NULL, minPS = 0.01, maxPS = 0.99)

Arguments

trt	Treatment received; vector of size n (observations) with treatment coded as 0/1
x.ps	A matrix of p.ps baseline covariates (plus a leading column of 1 for the intercept); dimension n by p.ps + 1 (covariates in the propensity score model plus intercept)
xnew	A matrix of p.ps baseline covariates (plus a leading column of 1 for the intercept) for which we want PS predictions; dimension m (observations in the new data set) by p.ps + 1
minPS	A numerical value (in '[0, 1]') below which estimated propensity scores should be truncated. Default is 0.01.
maxPS	A numerical value (in '(0, 1]') above which estimated propensity scores should be truncated. Must be strictly greater than minPS. Default is 0.99.

Value

The estimated propensity score for each unit; vector of size n (if xnew is NULL) or m

precmed documentation built on Oct. 6, 2024, 1:07 a.m.