estimateQ {drtmle}R Documentation

estimateQ

Description

Function to estimate initial outcome regression

Usage

estimateQ(
  Y,
  A,
  W,
  DeltaA,
  DeltaY,
  SL_Q,
  glm_Q,
  a_0,
  stratify,
  family,
  verbose = FALSE,
  returnModels = FALSE,
  se_cv = "none",
  se_cvFolds = 10,
  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).

SL_Q

A vector of characters or a list describing the Super Learner library to be used for the outcome regression.

glm_Q

A character describing a formula to be used in the call to glm for the outcome regression.

a_0

A list of fixed treatment values

stratify

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

family

A character passed to SuperLearner

verbose

A boolean indicating whether to print status updates.

returnModels

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

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.

validRows

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

...

Additional arguments (not currently used)


[Package drtmle version 1.1.0 Index]