estimatePi: 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))
)

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.

Arguments

Y: outcome
A: binary treatment indicator
W: covariate matrix observed on everyone
condSetNames: Variables to include as predictors of missingness in W.stage2, any combination of Y, A, and either W (for all covariates in W) or individual covariate names in W
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 W
piform: parametric regression formula for estimating pi
pi.SL.library: super learner library for estimating pi
id: Identifier of independent units of observation, e.g., clusters
V: number of cross validation folds for estimating pi using super learner
discreteSL: Use discrete super learning when TRUE, otherwise ensemble super learning
verbose: When TRUE prints informational messages
pi: optional vector of user-specified probabilities
obsWeights: optional weights for evaluating pi