estimateG {drtmle}  R Documentation 
estimateG
Description
Function to estimate propensity score
Usage
estimateG(A, W, DeltaY, DeltaA, SL_g, glm_g, a_0, tolg, stratify = FALSE,
validRows = NULL, verbose = FALSE, returnModels = FALSE, Qn = NULL,
adapt_g = FALSE, se_cv = "none", se_cvFolds = 10)
Arguments
A 
A vector of binary treatment assignment (assumed to be equal to 0 or 1) 
W 
A 
DeltaY 
Indicator of missing outcome (assumed to be equal to 0 if missing 1 if observed) 
DeltaA 
Indicator of missing treatment (assumed to be equal to 0 if missing 1 if observed) 
SL_g 
A vector of characters describing the super learner library to be
used for each of the regression ( 
glm_g 
A character describing a formula to be used in the call to

a_0 
A vector of fixed treatment values at which to return marginal mean estimates. 
tolg 
A numeric indicating the minimum value for estimates of the propensity score. 
stratify 
A 
validRows 
A 
verbose 
A boolean indicating whether to print status updates. 
returnModels 
A boolean indicating whether to return model fits for the outcome regression, propensity score, and reduceddimension regressions. 
Qn 
A 
adapt_g 
A boolean indicating whether propensity score is adaptive to outcome regression. 
se_cv 
Should crossvalidated nuisance parameter estimates be used
for computing standard errors?
Options are 
se_cvFolds 
If crossvalidated nuisance parameter estimates are used
to compute standard errors, how many folds should be used in this computation.
If 