Fits GLM model with treatment weights $$ w(A)= \sum_a I(A=a)/P(A=a|X) $$, computes standard errors via influence functions that are returned as the IID argument. Propensity scores are fitted using either logistic regression (glm) or the multinomial model (mlogit) when more than two categories for treatment. The treatment needs to be a factor and is identified on the rhs of the "treat.model".
glm_IPTW(
formula,
data,
treat.model = NULL,
family = binomial(),
id = NULL,
weights = NULL,
estpr = 1,
pi0 = 0.5,
...
)for glm
data frame for risk averaging
propensity score model (binary or multinomial)
of glm (logistic regression)
cluster id for standard errors
may be given, and then uses weights*w(A) as the weights
to estimate propensity scores and get infuence function contribution to uncertainty
fixed simple weights
arguments for glm call
Thomas Scheike
Also works with cluster argument.