RejStep: CMB validation step
In gfboost: Gradient-Free Gradient Boosting

Description Usage Arguments Details Value References

Validation step to combine different SingBoost models.

RejStep(
  D,
  nsing,
  Bsing = 1,
  ind,
  sing = FALSE,
  singfam = Gaussian(),
  evalfam = Gaussian(),
  M = 10,
  m_iter = 100,
  kap = 0.1,
  LS = FALSE,
  best = 1
)

`D`	Data matrix. Has to be an n \times (p+1)-dimensional data frame in the format (X,Y). The X-part must not contain an intercept column containing only ones since this column will be added automatically.
`nsing`	Number of observations (rows) used for the SingBoost submodels.
`Bsing`	Number of subsamples based on which the SingBoost models are validated. Default is 1. Not to confuse with parameter `B` for the Stability Selection.
`ind`	Vector with indices for dividing the data set into training and validation data.
`sing`	If `sing=FALSE` and the `singfam` family is a standard Boosting family that is contained in the package `mboost`, the CMB aggregation procedure is executed for the corresponding standard Boosting models.
`singfam`	A SingBoost family. The SingBoost models are trained based on the corresponding loss function. Default is `Gaussian()` (squared loss).
`evalfam`	A SingBoost family. The SingBoost models are validated according to the corresponding loss function. Default is `Gaussian()` (squared loss).
`M`	An integer between 2 and `m_iter`. Indicates that in every M-th iteration, a singular iteration will be performed. Default is 10.
`m_iter`	Number of SingBoost iterations. Default is 100.
`kap`	Learning rate (step size). Must be a real number in ]0,1]. Default is 0.1 It is recommended to use a value smaller than 0.5.
`LS`	If a `singfamily` object that is already provided by `mboost` is used, the respective Boosting algorithm will be performed in the singular iterations if `Ls` is set to `TRUE`. Default is `FALSE`.
`best`	Needed in the case of localized ranking. The parameter `K` of the localized ranking loss will be computed by best \cdot n (rounded to the next larger integer). Warning: If a parameter `K` is inserted into the `LocRank` family, it will be ignored when executing SingBoost.

Divides the data set into a training and a validation set. The SingBoost models are computed on the training set and evaluated on the validation set based on the loss function corresponding to the selected Boosting family.