SL.glm.theta: SL Wrapper for Estimation of Cond. Prob. of X=0 Given W
In tmle.npvi: Targeted Learning of a NP Importance of a Continuous Exposure
Description
Arguments
Value
Author(s)
See Also
Description
Prediction algorithm wrapper for SuperLearner, for the estimation of the
conditional expectation of Y given (X,W).
Arguments
Y
X
newX
family
obsWeights
...
Value
Returns a fitted object.
Author(s)
Antoine Chambaz, Pierre Neuvial
See Also
learnTheta, predict.SL.glm.theta
tmle.npvi documentation built on May 1, 2019, 6:50 p.m.
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Description Arguments Value Author(s) See Also
Description
Prediction algorithm wrapper for SuperLearner, for the estimation of the conditional expectation of Y given (X,W).
Arguments
Y |
|
X |
|
newX |
|
family |
|
obsWeights |
|
... |
Value
Returns a fitted object.
Author(s)
Antoine Chambaz, Pierre Neuvial
See Also
learnTheta, predict.SL.glm.theta
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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