estimategrn {drtmle}

estimategrn

Description

Estimates the reduced dimension regressions necessary for the additional fluctuations.

Usage

estimategrn(Y, A, W, DeltaA, DeltaY, Qn, gn, SL_gr, tolg, glm_gr, a_0,
  reduction, returnModels, validRows)

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.

gn

A list of propensity regression estimates evaluated on observed data.

SL_gr

A vector of characters or a list describing the Super Learner library to be used for the reduced-dimension propensity score.

tolg

A numeric indicating the minimum value for estimates of the propensity score.

glm_gr

A character describing a formula to be used in the call to glm for the second reduced-dimension regression. Ignored if SL_gr!=NULL.

a_0

A list of fixed treatment values .

reduction

A character equal to 'univariate' for a univariate misspecification correction or 'bivariate' for the bivariate version.

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.