iTMLE.updater.glm: iTMLE glm learner / updater

In osofr/stremr: Streamlined Estimation for Static, Dynamic and Stochastic Treatment Regimes in Longitudinal Data

Description

Performs an SDR update using a main-terms GLM (logistic regression with stats::glm). This function is passed along as a separate learner to the SuperLearner implementation in origami package

Usage

iTMLE.updater.glm(Y, X, newX, family, obsWeights, ...)

## S3 method for class 'iTMLE.updater.glm'
predict(object, newdata, offset, ...)

Arguments

Y	Input outcomes
X	Input design matrix with training data. Must contain a column named "offset", which contains the offsets converted to logit-linear scale.
newX	Input design matrix with test data. Same requirement as for X: must contain a column named "offset", which contains the offsets converted to logit-linear scale.
family	Link function (ignored).
obsWeights	Row-specific weights
...	Additional arguments to be passed on to origami package.
object	Results of calling iTMLE.updater.glm.
newdata	Design matrix with test data for which predictions should be obtained. May contain a column named "offset", instead of it being passed as a separate argument.
offset	Offset (on logit scale if using logistic regression update).

osofr/stremr documentation built on Jan. 25, 2022, 8:07 a.m.