estimateQ: Initial Estimation of Q portion of the Likelihood

estimateQR Documentation

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 May 29, 2024, 6:38 a.m.