path.singboost {gfboost}

Coefficient paths for SingBoost

Description

Runs SingBoost but saves the coefficients paths. If no coefficient path plot is needed, just use singboost.

Usage

path.singboost(
  D,
  M = 10,
  m_iter = 100,
  kap = 0.1,
  singfamily = Gaussian(),
  best = 1,
  LS = FALSE
)

Arguments

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.

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.

singfamily

A Boosting family corresponding to the target loss function. See .mboost for families corresponding to standard loss functions. May also use the loss functions for ranking losses provided in this package. Default is Gaussian() for which SingBoost is just standard L_2-Boosting.

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.

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.

Value

Selected variables

Names of the selected variables.

Coefficients

The selected coefficients as an (p+1)-dimensional vector (i.e., including the zeroes).

Freqs

Selection frequencies and a matrix for intercept and coefficient paths, respectively.

Intercept path

The intercept path as an m_{iter}-dimensional vector.

Coefficient path

The coefficient paths as a 2 \cdot m_{iter} \times 2-dimensional matrix.