estimateQrn {drtmle}  R Documentation 
Estimates the reduced dimension regressions necessary for the fluctuations of g
estimateQrn( Y, A, W, DeltaA, DeltaY, Qn, gn, glm_Qr, SL_Qr, family = stats::gaussian(), a_0, returnModels, 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) 
Qn 
A list of outcome regression estimates evaluated on observed data. If NULL then 0 is used for all Qn (as is needed to estimate reduced dimension regression for adaptive_iptw) 
gn 
A list of propensity regression estimates evaluated on observed data 
glm_Qr 
A character describing a formula to be used in the call to

SL_Qr 
A vector of characters or a list describing the Super Learner library to be used for the first reduceddimension regression. 
family 
Should be gaussian() unless called from adaptive_iptw with
binary 
a_0 
A list of fixed treatment values. 
returnModels 
A boolean indicating whether to return model fits for the outcome regression, propensity score, and reduceddimension regressions. 
validRows 
A 