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".
Usage
glm_IPTW(
formula,
data,
treat.model = NULL,
family = binomial(),
id = NULL,
weights = NULL,
estpr = 1,
pi0 = 0.5,
...
)Arguments
- formula
for glm
- data
data frame for risk averaging
- treat.model
propensity score model (binary or multinomial)
- family
of glm (logistic regression)
- id
cluster id for standard errors
- weights
may be given, and then uses weights*w(A) as the weights
- estpr
to estimate propensity scores and get infuence function contribution to uncertainty
- pi0
fixed simple weights
- ...
arguments for glm call