eeFunTV_IPTW: Estimating Function for IPTW

In BarkleyBG/stabilizedinterference: Stabilized IPW Estimators for Causal Effects with Partial Interference

Description

This function is to be passed into geex::m_estimate

Usage

eeFunTV_IPTW(data, trt_model_obj, num_fixefs, var_names, alpha,
  x_levels = NULL, integrate_alphas, randomization_probability, weight_type)

Arguments

data	the dataframe. Will be coerced from "tbl_df" to data.frame.
trt_model_obj	The fitted model object (usually a glm).
num_fixefs	Number of fixed effect parameters from treatment model. Perhaps unncessaary coding.
var_names	A list of names for outcome, treatment, clustering, and perhaps participation.
alpha	One of the allocations at a time
x_levels	default NULL unless there are factos in design matrix. From grab_design_levels.
integrate_alphas	true
randomization_probability	usually 1. e.g. 2/3 in Perez-Heydrich et al. (2014) Biometrics
weight_type	Estimators as presented in Liu, Hudgens, and Becker-Dreps (2016) Biometrika. Select "HT" for unstabilized weights. Select "Hajek1" or "Hajek2" for stabilized weights. Select "HT_TV" for the estimators presented in Tchetgen Tchetgen and VanderWeele (2012) SMMR and Perez-Heydrich et al. (2014) Biometrics, which in general target estimands different from those in Liu, Hudgens, and Becker-Dreps (2016) Biometrika.

BarkleyBG/stabilizedinterference documentation built on May 23, 2019, 8:37 a.m.