estimatePi {twoStageDesignTMLE} | R Documentation |
estimatePi
Description
Typically not called directly by the user. Function for modeling the two-stage missingness mechanism and evaluating conditional probabilities for each observation
Usage
estimatePi(
Y,
A,
W,
condSetNames,
W.Q,
Delta.W,
V.msm = NULL,
piform,
pi.SL.library,
id,
V,
discreteSL,
verbose,
pi = NULL,
obsWeights = rep(1, nrow(W))
)
Arguments
Y |
outcome |
A |
binary treatment indicator |
W |
covariate matrix observed on everyone |
condSetNames |
Variables to include as predictors of missingness
in |
W.Q |
additional covariates based on preliminary outcome regression |
Delta.W |
binary indicator of missing second stage covariates |
V.msm |
optional additional covariates to condition on beyond |
piform |
parametric regression formula for estimating |
pi.SL.library |
super learner library for estimating |
id |
Identifier of independent units of observation, e.g., clusters |
V |
number of cross validation folds for estimating |
discreteSL |
Use discrete super learning when |
verbose |
When |
pi |
optional vector of user-specified probabilities |
obsWeights |
optional weights for evaluating pi |
Value
list containing the predicted probabilities, estimation method coefficients in parametric regression model (if piform supplied), indicator of whether discrete or ensemble SL was used.