estimateG {tmle}

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