estimateQrn {drtmle}R Documentation

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.


[Package drtmle version 1.1.0 Index]