estimateQrn: estimateQrn

In benkeser/drtmle: Doubly-Robust Nonparametric Estimation and Inference

estimateQrn

Description

Estimates the reduced dimension regressions necessary for the fluctuations of g

Usage

estimateQrn(Y, A, W, DeltaA, DeltaY, Qn, gn, glm_Qr, SL_Qr,
  family = stats::gaussian(), a_0, returnModels, validRows = NULL)

Arguments

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 data.frame of named covariates
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 glm for the first reduced-dimension regression. Ignored if SL_gr!=NULL.
SL_Qr	A vector of characters or a list describing the Super Learner library to be used for the first reduced-dimension regression.
family	Should be gaussian() unless called from adaptive_iptw with binary Y.
a_0	A list of fixed treatment values.
returnModels	A boolean indicating whether to return model fits for the outcome regression, propensity score, and reduced-dimension regressions.
validRows	A list of length cvFolds containing the row indexes of observations to include in validation fold.

benkeser/drtmle documentation built on Jan. 6, 2023, 11:40 a.m.