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.
estimateQ(Y, Z, A, W, Delta, Q, Qbounds, Qform, maptoYstar, SL.library, cvQinit,
family, id, V, verbose, discreteSL, obsWeights)
\(nx3\) 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
‘binomial’ for targeting with logistic fluctuation, ‘gaussian’ for linear fluctuation
coefficients for each term in working model used for initial estimation of Q if glm used.
type of estimation procedure
continuous or binary outcome variable
optional binary indicator for intermediate covariate for conrolled direct effect estimation
binary treatment indicator, 1 - treatment, 0 - control
vector, matrix, or dataframe containing baseline covariates
indicator of missing outcome. 1 - observed, 0 - missing
3-column matrix (Q(A,W), Q(0,W), Q(1,W))
Bounds on predicted values for Q, set to alpha for logistic fluctuation, or range(Y) if not user-supplied
regression formula of the form Y~A+W
if TRUE indicates continuous Y values should be shifted and scaled to fall between (0,1)
specification of prediction algorithms, default is (‘SL.glm’, ‘SL.glmnet’, ‘tmle.SL.dbarts2’). In practice, including more prediction algorithms in the library improves results.
logical, whether or not to estimate cross-validated values for initial Q, default=TRUE
family specification for regressions, generally ‘gaussian’ for continuous oucomes, ‘binomial’ for binary outcomes
subject identifier
Number of cross-validation folds for Super Learning
status message printed if set to TRUE
If true, returns discrete SL estimates, otherwise ensemble estimates. Ignored when SL is not used.
sampling weights
Susan Gruber
tmle,
estimateG,
calcParameters,
tmleMSM,
calcSigma