Definition of the initial number of boosting iterations, step size and other hyper-parameters for boosting algorithms.

```
boost_control(mstop = 100, nu = 0.1,
risk = c("inbag", "oobag", "none"), stopintern = FALSE,
center = TRUE, trace = FALSE)
```

An object of class `boost_control`

, a list.

- mstop
an integer giving the number of initial boosting iterations. If

`mstop = 0`

, the offset model is returned.- nu
a double (between 0 and 1) defining the step size or shrinkage parameter. The default is probably too large for many applications with

`family = Poisson()`

and a smaller value is better.- risk
a character indicating how the empirical risk should be computed for each boosting iteration.

`inbag`

leads to risks computed for the learning sample (i.e., all non-zero weights),`oobag`

to risks based on the out-of-bag (all observations with zero weights) and`none`

to no risk computations at all.- stopintern
a logical that defines if the boosting algorithm stops internally when the out-of-bag risk in one iteration is larger than the out-of-bag risk in the iteration before. Can also be a positive number giving the risk difference that needs to be exceeded.

- center
deprecated. A logical indicating if the numerical covariates should be mean centered before fitting. Only implemented for

`glmboost`

. In`blackboost`

centering is not needed. In`gamboost`

centering is only needed if`bols`

base-learners are specified without intercept. In this case centering of the covariates is essential and should be done manually (at the moment). Will be removed in favour of a corresponding argument in`glmboost`

in the future (and gives a warning).- trace
a logical triggering printout of status information during the fitting process.

Objects returned by this function specify hyper-parameters of the
boosting algorithms implemented in `glmboost`

,
`gamboost`

and `blackboost`

(via the `control`

argument).

`mboost`