estimateQ: Initial Estimation of Q portion of the Likelihood

In tmle: Targeted Maximum Likelihood Estimation

Initial Estimation of Q portion of the Likelihood

Description

An internal function called by the tmle function to obtain an initial estimate of the Q portion of the likelihood based on user-supplied matrix values for predicted values of (counterfactual outcomes) Q(0,W),Q(1,W), or a user-supplied regression formula, or based on a data-adaptively selected SuperLearner fit. In the absence of user-supplied values, a user-supplied regression formula takes precedence over data-adaptive super-learning. The default is to return cross-validated predictions.

Usage

estimateQ(Y, Z, A, W, Delta, Q, Qbounds, Qform, maptoYstar, SL.library, cvQinit, 
    family, id, V, verbose, discreteSL, obsWeights)

Arguments

Y	continuous or binary outcome variable
Z	optional binary indicator for intermediate covariate for conrolled direct effect estimation
A	binary treatment indicator, 1 - treatment, 0 - control
W	vector, matrix, or dataframe containing baseline covariates
Delta	indicator of missing outcome. 1 - observed, 0 - missing
Q	3-column matrix (Q(A,W), Q(0,W), Q(1,W))
Qbounds	Bounds on predicted values for Q, set to alpha for logistic fluctuation, or range(Y) if not user-supplied
Qform	regression formula of the form Y~A+W
maptoYstar	if TRUE indicates continuous Y values should be shifted and scaled to fall between (0,1)
SL.library	specification of prediction algorithms, default is (‘SL.glm’, ‘SL.glmnet’, ‘tmle.SL.dbarts2’). In practice, including more prediction algorithms in the library improves results.
cvQinit	logical, whether or not to estimate cross-validated values for initial Q, default=TRUE
family	family specification for regressions, generally ‘gaussian’ for continuous oucomes, ‘binomial’ for binary outcomes
id	subject identifier
V	Number of cross-validation folds for Super Learning
verbose	status message printed if set to TRUE
discreteSL	If true, returns discrete SL estimates, otherwise ensemble estimates. Ignored when SL is not used.
obsWeights	sampling weights

Value

Q	nx3 matrix, columns contain the initial estimate of [Q(A,W)=E(Y|A=a,W), Q(0,W)=E(Y|A=0,W), Q(1,W)=E(Y|A=1,W)]. For controlled direct estimation, nx5 matrix, E(Y|Z,A,W), evaluated at (z,a), (0,0), (0,1), (1,0), (1,1) on scale of linear predictors
Qfamily	‘binomial’ for targeting with logistic fluctuation, ‘gaussian’ for linear fluctuation
coef	coefficients for each term in working model used for initial estimation of Q if glm used.
type	type of estimation procedure

Author(s)

Susan Gruber

See Also

tmle, estimateG, calcParameters, tmleMSM, calcSigma

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