estimateQ_loop {drtmle}  R Documentation 
A helper loop function to clean up the internals of drtmle
function.
estimateQ_loop(validRows, Y, A, W, DeltaA, DeltaY, verbose, returnModels, SL_Q,
a_0, stratify, glm_Q, family, use_future, se_cv, se_cvFolds)
validRows 
A 
Y 
A vector of continuous or binary outcomes. 
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) 
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. 
SL_Q 
A vector of characters or a list describing the Super Learner
library to be used for the outcome regression. See

a_0 
A list of fixed treatment values. 
stratify 
A 
glm_Q 
A character describing a formula to be used in the call to

family 
Should be gaussian() unless called from adaptive_iptw with
binary 
use_future 
Boolean indicating whether to use 
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 