estimateG_loop {drtmle}

estimateG_loop

Description

Helper function to clean up internals of drtmle function

Usage

estimateG_loop(validRows, A, W, DeltaA, DeltaY, tolg, verbose, stratify,
  returnModels, SL_g, glm_g, a_0, Qn, adapt_g, use_future, se_cv = "none",
  se_cvFolds = 10)

Arguments

validRows

A list of length cvFolds containing the row indexes of observations to include in validation fold.

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)

tolg

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

verbose

A boolean indicating whether to print status updates.

stratify

A boolean indicating whether to estimate the missing outcome regression separately for observations with A equal to 0/1 (if TRUE) or to pool across A (if FALSE).

returnModels

A boolean indicating whether to return model fits for the outcome regression, propensity score, and reduced-dimension regressions.

SL_g

A vector of characters describing the super learner library to be used for each of the regression (DeltaA, A, and DeltaY). To use the same regression for each of the regressions (or if there is no missing data in A nor Y), a single library may be input.

glm_g

A character describing a formula to be used in the call to glm for the propensity score.

a_0

A vector of fixed treatment values at which to return marginal mean estimates.

Qn

A list of estimates of the outcome regression for each value in a_0. Only needed if adapt_g = TRUE.

adapt_g

A boolean indicating whether propensity score is adaptive to outcome regression.

use_future

Should future be used for parallelization?

se_cv

Should cross-validated nuisance parameter estimates be used for computing standard errors? Options are "none" = no cross-validation is performed; "partial" = only applicable if Super Learner is used for nuisance parameter estimates; "full" = full cross-validation is performed. See vignette for further details. Ignored if cvFolds > 1, since then cross-validated nuisance parameter estimates are used by default and it is assumed that you want full cross-validated standard errors.

se_cvFolds

If cross-validated nuisance parameter estimates are used to compute standard errors, how many folds should be used in this computation. If se_cv = "partial", then this option sets the number of folds used by the SuperLearner fitting procedure.