estimateG_loop {drtmle}  R Documentation 
estimateG_loop
Description
Helper function to clean up internals of drtmle
function
Usage
estimateG_loop(validRows, A, W, DeltaA, DeltaY, tolg, verbose, stratify,
returnModels, SL_g, glm_g, a_0, Qn, adapt_g, use_future, se_cv = "none",
se_cvFolds = 10)
Arguments
validRows 
A 
A 
A vector of binary treatment assignment (assumed to be equal to 0 or 1) 
W 
A 
DeltaA 
Indicator of missing treatment (assumed to be equal to 0 if missing 1 if observed) 
DeltaY 
Indicator of missing outcome (assumed to be equal to 0 if missing 1 if observed) 
tolg 
A numeric indicating the minimum value for estimates of the propensity score. 
verbose 
A boolean indicating whether to print status updates. 
stratify 
A 
returnModels 
A boolean indicating whether to return model fits for the outcome regression, propensity score, and reduceddimension regressions. 
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. 
Qn 
A 
adapt_g 
A boolean indicating whether propensity score is adaptive to outcome regression. 
use_future 
Should 
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 