estimateG: Estimate Treatment or Missingness Mechanism

In tmle: Targeted Maximum Likelihood Estimation

Estimate Treatment or Missingness Mechanism

Description

An internal function called by the tmle function to obtain an estimate of conditional treatment assignment probabiliites P(A=1|W), and conditional probabilites for missingness, P(Delta=1|A,W). The estimate can be based on user-supplied values, a user-supplied regression formula, or a data-adaptive super learner fit. If the SuperLearner package is not available, and there are no user-specifications, estimation is carried out using main terms regression with glm. These main terms-based estimates may yield poor results.

Usage

estimateG(d, g1W, gform, SL.library, id, V, verbose, message, 
	outcome="A", newdata=d, discreteSL, obsWeights)

Arguments

d	dataframe with binary dependent variable in the first column, predictors in remaining columns
g1W	vector of values for P(A=1|W), P(Z=1|A,W), or P(Delta=1|Z,A,W)
gform	regression formula of the form A~W1, (dependent variable is one of A,Z,D) if specified this overrides the call to SuperLearner
SL.library	vector of prediction algorithms used by SuperLearner, default value is (‘SL.glm’, ‘tmle.SL.dbarts.k.5’, ‘SL.gam’)
id	subject identifier
V	Number of cross validation folds for Super Learning
verbose	status messages printed if set to TRUE
message	text specifies whether treatment or missingness mechanism is being estimated
outcome	A, D, Z to indicate which quantity is being estimated.
newdata	optional dataset to be used for prediction after fitting on d.
discreteSL	If true, returns discrete SL estimates, otherwise ensemble estimates. Ignored when SL is not used.
obsWeights	sampling weights

Value

g1W	a vector containing values for P(A=1|W), matrix for P(Z=1|A,W), evaluated at A=0, A=1, or matrix P(Delta=1|Z,A,W)) evaluated at (0,0), (0,1), (1,0), (1,1)
coef	coefficients for each term in the working model used for estimation if glm was used
type	estimation procedure

Author(s)

Susan Gruber

See Also

tmle, estimateQ, calcParameters, tmleMSM, calcSigma

tmle documentation built on Aug. 23, 2023, 1:08 a.m.