estimateQ {drtmle}  R Documentation 
Function to estimate initial outcome regression
estimateQ(Y, A, W, DeltaA, DeltaY, SL_Q, glm_Q, a_0, stratify, family,
verbose = FALSE, returnModels = FALSE, se_cv = "none",
se_cvFolds = 10, validRows = NULL, ...)
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). 
SL_Q 
A vector of characters or a list describing the Super Learner library to be used for the outcome regression. 
glm_Q 
A character describing a formula to be used in the call to

a_0 
A list of fixed treatment values 
stratify 
A 
family 
A character passed to 
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. 
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 
validRows 
A 
... 
Additional arguments (not currently used) 