boost_control: Specify Controls for the Boosting Algorithm
In CoxFlexBoost: Boosting Flexible Cox Models (with Time-Varying Effects)

Description Usage Arguments Value See Also

Specify control and tuning parameters of the boosting algorithm.

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boost_control(mstop = 100, nu = 0.1, maxit = 30000, risk = c("inbag","oobag", "none"),
    which.offset = c("mle", "zero"), savedata = TRUE, trace = TRUE,
    parallel = require("multicore"), hardStop = TRUE, save_ensembless=TRUE)

`mstop`	integer. Initial number of boosting iterations
`nu`	double. Step-size between 0 and 1
`maxit`	integer. Controls the maximum number of iterations that are used for numerical optimazation via `optim(..., method = "BFGS")`.
`risk`	character. Determines how the empirical risk should be computed. It can take the values `risk = "inbag"`, i.e., the risk is computed for the learning sample, `risk = "oobag"`, i.e., the risk is computed for the validation sample and `risk = "none"`.
`which.offset`	character. Indictating the choice of offset. This can be either the maximum likelihood estimator of the null model (`which.offset = "mle"`) or zero (`which.offset = "zero"`).
`savedata`	logic. Indicating whether the data should be saved in the returned `cfboost` object.
`trace`	logic. Determining if status information should be printed during estimation of the model.
`parallel`	logic. enable parallelization using multicore technology for crossvalidation (in function `cv`). If multicore is available parallelization is used per default but can be switched off.
`hardStop`	logic. Indicates if the initial value of `mstop` is the maximal number of iterations (`mstop = TRUE`) or if it should be increased if the algorithm did not converge until `mstop`. The latter is only possible with `risk = "oobag"`.
`save_ensembless`	logic. Indicating if the list of coefficients for the selected base-learners should be saved and returned. This list is generally needed but can be suppressed to reduce memory usage (not recommended).