CV.SuperLearner.hazard: Computes the V-fold cross-validated hazard estimation using...
In ecpolley/SuperLearner_Old: Super Learner Prediction

Description Usage Arguments Value Author(s) See Also

V-fold Cross validated hazard estimation using binary Super Learner

CV.SuperLearner.hazard(N.delta, n.controls=1, time.df=5, time, X, SL.library, outside.V=20, inside.V = 20, shuffle=FALSE, verbose=FALSE, family=binomial(), method="NNLS", id=NULL, save.fit.library=FALSE, trim.logit=0.0001, discreteTime = TRUE, obsWeights = NULL)

`N.delta`	Discrete survival process. often created by `create.discrete`
`n.controls`	number of randomly sampled observations from risk set using `incidence.sample`. Set to 0 for entire sample.
`time.df`	degrees of freedom for the `gam` model. Currently not implemented
`time`	the time variable. often `delta.u` from `create.discrete`
`X`	the baseline variables. Do not include the outcome nor time
`SL.library`	algorithms for binary Super Learner
`outside.V`	An integer for the number of folds to split the data into
`inside.V`	An integer for the number of folds each Super Learner should use
`shuffle`	shuffle rows in data frame before creating cross validation folds
`verbose`	more detailed output
`family`	binomial
`discreteTime`	currently not implemented
`method`	Loss function for combining prediction in the library. Currently either "NNLS" (the default) or "NNloglik". NNLS is non-negative least squares and will work for both gaussian and binomial. NNloglik is a non-negative binomial likelihood maximization
`id`	subject identification variable
`save.fit.library`	logical variable for saving the fit of each algorithm in `SL.library`
`trim.logit`	Only used if `method="NNloglik"`. specifies a truncation level for the logit function for stability.
`obsWeights`	observation weights

`CV.fit.SL`	A list containing the output from each `SuperLearner`
`pred.SL`	The V-fold cross-validation super learner predictions for the outcome. These can be used to estimate the honest cross-validated risk
`pred.discreteSL`	The V-fold cross-validated discrete super learner prediction for the outcome. The discrete super learner selects the algorithm with the minimum internal cross-validated risk estimate. See output value `whichDiscreteSL` for the algorithm name associated with each fold
`whichDiscreteSL`	The prediction algorithm selected in each outside V fold as the discrete super learner
`pred.library`	The V-fold cross-validation predictions for the outcome from all algorithms in the library
`coef.SL`	a matrix of coefficients in the SuperLearner across the V folds
`folds`	a list with the cross-validation splits
`call`	the function call