estimategrn
Estimates the reduced dimension regressions necessary for the additional fluctuations.
estimategrn(Y, A, W, DeltaA, DeltaY, Qn, gn, SL_gr, tolg, glm_gr, a_0, reduction, returnModels, validRows)
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.