Propensity score based on a multivariate logistic regression with LASSO penalization on the two-way interactions
glm.ps(trt, x.ps, xnew = NULL, minPS = 0.01, maxPS = 0.99)The trimmed propensity score for each unit; vector of size n (if xnew is NULL) or m
Treatment received; vector of size n (observations) with treatment coded as 0/1
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)
Matrix of p.ps baseline covariates (plus a leading column of 1 for the intercept)
for which we want propensity scores predictions; dimension m (observations in the new data set) by p.ps + 1
A numerical value (in `[0, 1]`) below which estimated propensity scores should be
truncated. Default is 0.01.
A numerical value (in `(0, 1]`) above which estimated propensity scores should be
truncated. Must be strictly greater than minPS. Default is 0.99.