Propensity score estimation with a linear model

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