Function to estimate initial outcome regression
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, ...)A vector of continuous or binary outcomes.
A vector of binary treatment assignment (assumed to be equal to 0 or 1).
A data.frame of named covariates.
Indicator of missing treatment (assumed to be equal to 0 if missing 1 if observed).
Indicator of missing outcome (assumed to be equal to 0 if missing 1 if observed).
A vector of characters or a list describing the Super Learner library to be used for the outcome regression.
A character describing a formula to be used in the call to
glm for the outcome regression.
A list of fixed treatment values
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).
A character passed to SuperLearner
A boolean indicating whether to print status updates.
A boolean indicating whether to return model fits for the outcome regression, propensity score, and reduced-dimension regressions.
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.
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.
A list of length cvFolds containing the row
indexes of observations to include in validation fold.
Additional arguments (not currently used)