estimateQrn function

estimateQrn

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)

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.