CMB.stabpath: CMB stability paths
In gfboost: Gradient-Free Gradient Boosting

Description Usage Arguments Value References

Draws a Stability plot for CMB.

CMB.stabpath(
  D,
  nsing,
  Bsing = 1,
  alpha = 1,
  singfam = Gaussian(),
  evalfam = Gaussian(),
  sing = FALSE,
  Mseq,
  m_iter = 100,
  kap = 0.1,
  LS = FALSE,
  best = 1,
  wagg,
  robagg = FALSE,
  lower = 0,
  B,
  ncmb,
  ...
)

`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.
`alpha`	Optional real number in ]0,1]. Defines the fraction of best SingBoost models used in the aggregation step. Default is 1 (use all 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).
`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.
`Mseq`	A vector of different values for M.
`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.
`wagg`	Type of row weight aggregation. `'weights1'` indicates that the selection frequencies of the (best) SingBoost models are averaged. `'weights2'` respects the validation losses for each model and downweights the ones with higher validation losses.
`robagg`	Optional. If setting `robagg=TRUE`, the best SingBoost models are ignored when executing the aggregation to avoid inlier effects. Only reasonable in combination with `lower`.
`lower`	Optional argument. Only reasonable when setting `robagg=TRUE`. `lower` is a real number in [0,1[ (a rather small number is recommended) and indicates that the aggregation ignores the SingBoost models with the best performances to avoid possible inlier effects.
`B`	Number of subsamples of size n_{cmb} of the training data for CMB aggregation.
`ncmb`	Number of samples used for `CMB`. Integer that must be smaller than the number of samples in `Dtrain`.
`...`	Optional further arguments